Bayesian Analysis of Agricultural Field Experiments
Abstract
Summary: The paper describes Bayesian analysis for agricultural field experiments, a topic that has received very little previous attention, despite a vast frequentist literature. Adoption of the Bayesian paradigm simplifies the interpretation of the results, especially in ranking and selection. Also, complex formulations can be analysed with comparative ease, by using Markov chain Monte Carlo methods. A key ingredient in the approach is the need for spatial representations of the unobserved fertility patterns. This is discussed in detail. Problems caused by outliers and by jumps in fertility are tackled via hierarchical t formulations that may find use in other contexts. The paper includes three analyses of variety trials for yield and one example involving binary data; none is entirely straightforward. Some comparisons with frequentist analyses are made.
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Claims the paper makes
1 of 42 cited sources checked · 1 supported · 0 flagged
- Findingown data
A simple RCB decomposition of the El Batán data suggests a strong spatial pattern in the residuals.
“A simple RCB decomposition of the data suggests a strong spatial pattern in the residuals, so that naõÈ ve variety estimates have little worth.”
Quote in the paperSubstantial claim - Findingnone given
Under the hierarchical-t formulation, 90% credible intervals are sometimes shorter than their counterparts, despite the extra modelling flexibility.
“Note that the credible intervals in the hierarchical-t formulation are sometimes shorter than their counterparts, despite the additional ¯exibility in the modelling.”
Quote in the paperNot a substantial claim - Findingreasoning
The standard deviations and corresponding standard errors for variety differences also agree closely between the basic Bayesian and frequentist extended first-differences analyses.
“The same holds for the standard deviations and corresponding standard errors for variety dierences.”
Quote in the paperNot a substantial claim - Findingown data
The data show evidence of aberrations, including outliers and jumps in fertility.
“However, there is also evidence that the data contain aberrations, in the form of outliers and jumps in fertility.”
Quote in the paperSubstantial claim - Findingcitation
The basic Bayesian decomposition closely agrees with the decomposition obtained when Gaussian fertility increments are replaced by Laplace increments or by Brownian motion.
“This agrees closely with the decomposition when Gaussian fertility increments are replaced by Laplace ones, as in Besag and Higdon (1993), or by Brownian motion, as described in Section 2.3.3.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Besag and Higdon (1993)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Findingcitation
The basic Bayesian formulation with Gaussian fertility and yield assumptions and a vague variety prior gives variety effects that closely agree with a superficially similar frequentist extended first-differences analysis.
“We note here that the basic Bayesian formulation, with Gaussian assumptions for fertility and yield but a vague prior applied to varieties (see equation (5.3) of Besag et al. (1995), with 0), produces variety eects that agree closely with those from a super®cially similar frequentist extended ®rst-dierences analysis (see Besag and Kempton (1986)).”
Quote in the paperNot a substantial claim0 of 2 sources checked - Findingown data
Some variety rankings differ substantially from those obtained in the RCB analysis.
“In places, the variety rankings dier substantially from those of the RCB analysis.”
Quote in the paperNot a substantial claim - Findingreasoning
Fitting random walks to fertilities in each column gives a fit very similar to that from the locally quadratic fertility model.
“Hence, the ®t is very similar to that obtained from the locally quadratic fertility model.”
Quote in the paperNot a substantial claim - Findingown data
With a Gaussian random walk for fertilities within each row, the posterior means of strawberry variety effects are indistinguishable from the naive estimates.
“If a Gaussian random walk is assumed for the fertilities within each row of the experiment, then the posterior means of the variety eects are indistinguishable from the naõÈ ve estimates.”
Quote in the paperSubstantial claim - Findingnone given
The posterior distribution of the anisotropy parameter in the strawberry Bayesian analysis implies heavy concentration on within-column adjustment.
“the posterior distribution of the anisotropy parameter implies heavy concentration on within-column adjustment.”
Quote in the paperNot a substantial claim - Findingnone given
With a Gaussian prior for variety effects in the strawberry trial, there is very substantial shrinkage in the posterior means.
“If a Gaussian prior is used for variety eects, there is very substantial shrinkage in the posterior means.”
Quote in the paperSubstantial claim - Findingnone given
With a Gaussian prior for variety effects, the variety effects for corn counties 2, 4 and 6 show substantial shrinkage towards 0.
“Note that, with a Gaussian prior for , the variety eects for counties 2, 4 and 6 show substantial shrinkage towards 0.”
Quote in the paperNot a substantial claim - Findingown data
There is compelling visual evidence of spatial variation in fertility in the morning-glory experiment.
“There is compelling visual evidence of spatial variation in fertility.”
Quote in the paperNot a substantial claim - Findingown data
All 80% credible intervals except for Rl cover 0 in the within-row random walk analysis.
“All 80% credible intervals, except for Rl, cover 0.”
Quote in the paperSubstantial claim - Findingown data
Posterior standard deviations for strawberry variety differences range between 0.54 and 0.68.
“The posterior standard deviations for variety dierences range between 0.54 and 0.68.”
Quote in the paperNot a substantial claim - Findingown data
Under the first-order two-dimensional Bayesian analysis with a uniform prior, the ranking of strawberry varieties agrees with the covariance analysis except for a marginal reversal of E and P.
“With the ®rst-order prior (2.5), the posterior means are as shown in the third row of Table 5, so that, apart from the marginal reversal of E and P, the ranking of varieties agrees with the covariance analysis.”
Quote in the paperNot a substantial claim - Findingnone given
In the combined analysis of the six corn trials, the posterior distributions of fertilities and asymmetry parameters agree closely with those from the separate analyses.
“In the combined analysis, the posterior distributions of the fertilities and the asymmetry parameters agree closely with those obtained previously.”
Quote in the paperNot a substantial claim - Findingnone given
In county 4 of the corn trials, the Bayesian fertility map shows little variation within columns.
“In the Bayesian analysis, the fertility Downloaded from https://academic.oup.com/jrsssb/article/61/4/691/7083194 by guest on 19 December 2023 map shows little variation within columns.”
Quote in the paperNot a substantial claim - Findingown data
These numerical results are rather insensitive to the dimensions of the lay-out.
“and these results are rather insensitive to the dimensions of the lay-out.”
Quote in the paperNot a substantial claim - Findingreasoning
The estimated fertility patterns for the two types of spatial analysis of the corn trials are mostly rather similar.
“it is not surprising that the estimated fertility patterns for the two types of spatial analysis are mostly rather similar.”
Quote in the paperNot a substantial claim - Findingown data
In practice, two or three extra layers of virtual plots appear adequate.
“In practice, two or three extra layers seem adequate.”
Quote in the paperNot a substantial claim - Findingnone given
Inference on variety effects is very similar in all cases (hierarchical-t and spatial mixture models).
“Inference on variety eects is very similar in all cases.”
Quote in the paperNot a substantial claim - Findingcitation
Besag and Kempton's (1986) frequentist spatial analysis yields a decomposition similar to (c).
“Not surprisingly, the frequentist spatial analysis in Besag and Kempton (1986) produces a decomposition close to (c).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Besag and Kempton (1986) source
- Findingnone given
Neither system of blocks in the strawberry trial allows for the hedge.
“Neither system of blocks allows for the hedge.”
Quote in the paperNot a substantial claim- Rests on an assumption: The hedge in the strawberry experiment diminishes the yields of plots adjacent to it.
- Findingreasoning
Using the pseudovariate method for the strawberry experiment increases the data for plots next to the hedge by 2.5.
“If we do that, the plots next to the hedge have their data increased by 2.5.”
Quote in the paperNot a substantial claim- Rests on an assumption: The hedge in the strawberry experiment diminishes the yields of plots adjacent to it.
- Findingown data
For the strawberry trial in Section 4, the basic spatial model gives a plausible analysis without modelling the hedge in detail.
“Also, although we think it is of interest in Section 4 that our basic spatial formulation produces a plausible analysis without detailed modelling of the eect of the hedge, the point is not exclusively Bayesian.”
Quote in the paperNot a substantial claim- The authors state this as an assumption: The design spreads treatments widely enough to limit confounding with spatial effects, and the spatial model is not so complex that it absorbs real treatment effects.
- Findingown data
In the winter wheat trial, the additive decompositions from the three analyses are more similar than different.
“The similarities between the decompositions are more striking than the dierences.”
Quote in the paperNot a substantial claim - Findingown data
In the morning-glory data, viability and abundance of seed are strongly interrelated.
“Fig. 3 suggests that, in the case of the morning-glory data, they are strongly interrelated.”
Quote in the paperSubstantial claim - Findingown data
In the second North Carolina trial, several but not all end plots have yields that are way out of line.
“Indeed several, but not all, end plots have yields so large as to be `way out of line'.”
Quote in the paperSubstantial claim - Findingnone given
Model averaging leads to relatively smooth fertility patterns.
“Model averaging leads to the relatively smooth fertility patterns that we have displayed.”
Quote in the paperNot a substantial claim - Findingcitation
In Besag and Higdon's (1993) balanced lattice square example, adding complete-block effects without justification can disrupt the flow of useful information between neighbouring plots.
“Indeed, even the inclusion of complete-block eects, if unwarranted, may break the ¯ow of useful information between adjacent plots, as seems to occur in the balanced lattice square example of Besag and Higdon (1993), section 4.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Besag and Higdon (1993)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Findingreasoning
Fertility patterns are smoother with the hierarchical-t model than with the spatial mixture specifications.
“Fertility patterns are smoother with the hierarchical-t model, although the posterior means for fertility eects are not entirely comparable with the quantities E i z ij j y plotted for the spatial mixture speci®cations, as explained in the previous paragraph.”
Quote in the paperNot a substantial claim - Findingown data
Robust variograms produced very similar results to the standard variograms.
“Robust variograms (Cressie, 1993) were also calculated and produced very similar results.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Cressie, 1993 source
- Findingown data
Of the six real data variograms for North Carolina corn, Figs 13(b), 13(e) and 13(f) arguably show evidence of a continually increasing variogram, indicating non-stationarity.
“Of the six real data variograms, Figs 13(b), 13(e) and 13(f ) arguably show evidence of a continually increasing variogram, indicating non-stationarity.”
Quote in the paperSubstantial claim - Findingown data
Experiments using plot splitting and one-dimensional Brownian motion yielded robust conclusions.
“However, as mentioned in the paper, we did experiment with plot splitting and with Brownian motion in one dimension and found the conclusions to be quite robust.”
Quote in the paperNot a substantial claim - Findingnone given
The intrinsic model handles the example from Littell et al. (1996) adequately.
“Note also that the intrinsic model seems to cope adequately with the example in Littell et al. (1996).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Littell et al. (1996)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Findingown data
Two-dimensional spatial analysis of the El Batán trial shows that similarities are confined within replicates.
“It is plausible that the layout of the El Bata n trial was three contiguous columns but a two-dimensional spatial analysis indicates that similarities only occur within replicates.”
Quote in the paperNot a substantial claim - Findingreasoning
In practice, we have observed such poor Monte Carlo estimation of a proper posterior with finite moments.
“We have seen this happen in practice and we sometimes take steps to guard against it.”
Quote in the paperNot a substantial claim - Findingnone given
Glasbey, Pearce and Mead's strawberry trial analyses yield conclusions similar to ours, except for shrinkage.
“There are some disagreements over the lay-out of the strawberry trial, despite which the analyses by Glasbey, Pearce and Mead seem to reach conclusions that are not materially dierent from our own, apart from the shrinkage that we would adopt in practice.”
Quote in the paperNot a substantial claim - Interpretationreasoning
The intrinsic case can be seen as the limit of a stationary autoregression with no shrinkage towards a common mean fertility level.
“Indeed, the intrinsic case can be interpreted as the limit of a stationary autoregression, in which there is no shrinkage towards a common mean level of fertility.”
Quote in the paperNot a substantial claim - Interpretationnone given
For interior plots, the mean structures in equations (2.3) and (2.5) correspond when ω_ij equals ρ for same-column adjacencies and ψ for same-row adjacencies.
“For interior plots, there is correspondence between the mean structures in equations (2.3) and (2.5) if ! ij for i $ j in the same column and !ij”
Quote in the paperNot a substantial claim - Interpretationown data
Naive variety estimates from the RCB decomposition have little worth.
“A simple RCB decomposition of the data suggests a strong spatial pattern in the residuals, so that naõÈ ve variety estimates have little worth.”
Quote in the paperSubstantial claim - Interpretationreasoning
For any fixed ρ between 0 and 1, the only indeterminacy in the model is its overall level.
“For any ®xed P 0, 1, the sole indeterminacy in this model is its overall level.”
Quote in the paperNot a substantial claim - Premisenone given
Spatial representations of the unobserved fertility patterns are a key ingredient in the approach.
“A key ingredient in the approach is the need for spatial representations of the unobserved fertility patterns.”
Quote in the paperSubstantial claim- Rests on an assumption: Generally, a crop performs much more similarly on two experimental units that are close together than on plots that are further apart.
- Interpretationown data
The pronounced fertility patterns in the morning-glory seed count trial suggest that a standard frequentist formulation would be inappropriate.
“The existence of pronounced fertility patterns suggests that a standard frequentist formulation would be inappropriate.”
Quote in the paperSubstantial claim - Interpretationnone given
Hierarchical-t formulations address problems caused by outliers and by jumps in fertility.
“Problems caused by outliers and by jumps in fertility are tackled via hierarchical-t formulations that may ®nd use in other contexts.”
Quote in the paperSubstantial claim - Interpretationreasoning
This correspondence implies that the variance of the difference between adjacent plot fertilities changes radically between the centre and edges of the array, at least when ρ is not 1/2.
“Unfortunately, this implies that, for example, var i À j for adjacent plots i and j in the same row or in the same column changes radically between the centre and the edges of the array, at least when % 1 2 .”
Quote in the paperSubstantial claim - Premisenone given
The AR(1) x AR(1) process, viewed as a conditional autoregression, has an intrinsic limit corresponding to independence of two-dimensional differences and the locally quadratic representation.
“Recall that, viewed as a conditional autoregression, the AR(1) Â AR(1) process has an intrinsic limit which corresponds to independence of two-dimensional dierences and the locally quadratic representation”
Quote in the paperSubstantial claim - Interpretationreasoning
The Bayesian fertility map supports the downturn in fertility at the left-hand end of the strawberry trial seen in the covariance analysis.
“supports the downturn in fertility at the left-hand end of the trial in the covariance analysis.”
Quote in the paperNot a substantial claim - Interpretationnone given
In agricultural applications with moderately large data sets, inverting large non-sparse matrices is computationally very demanding for routine use.
“However, in agricultural applications with moderately large data sets, such as in Section 5, the computational task of inverting large non-sparse matrices becomes very demanding for routine use.”
Quote in the paperNot a substantial claim - Interpretationown data
None of the strawberry results suggests that the experiment is very conclusive.
“None of these results suggests that the experiment is very conclusive.”
Quote in the paperSubstantial claim - Premisecitation
Empirical investigations of lattice-based fertility models by Cullis and Gleeson (1991), Kempton et al. (1994) and Grondona et al. (1996) are restricted to separable autoregressive integrated moving average processes, which excludes the authors' specific proposal.
“Empirical investigations of lattice-based fertility models are described by Cullis and Gleeson (1991), Kempton et al. (1994) and Grondona et al. (1996) but with a restriction to separable autoregressive integrated moving average processes, which excludes our speci®c proposal.”
Quote in the paperSubstantial claim0 of 3 sources checked - Interpretationreasoning
The t-formulation explains why there is not a general increase in credible interval widths despite the additional flexibility.
“This explains why there is not a general increase in the widths of the credible intervals, despite the additional ¯exibility.”
Quote in the paperSubstantial claim - Interpretationreasoning
The random walk interpretation implies that edge corrections are not needed when transferring to finite lay-outs.
“Note that the random walk interpretation implies that edge corrections do not arise in transferring to ®nite lay-outs.”
Quote in the paperNot a substantial claim - Interpretationreasoning
The hint of an interaction effect in the morning-glory analysis is likely an artefact of the disparity in sample sizes across colour intensities.
“At ®rst sight, there may be the hint of an interaction eect but this is likely to be an artefact of the above disparity.”
Quote in the paperSubstantial claim- The authors state this as an assumption: In the morning-glory model, plants produce seeds independently given the centred effects and plot fertilities.
- Rests on an assumption: Morning-glory plants are susceptible to competition through overtopping, but competition is not an issue at the spacing used in this experiment.
- The authors state this as an assumption: The morning-glory model postulates that seed-production probabilities follow a logistic form with additive centred effects and plot fertilities.
- Interpretationreasoning
Within-row adjustment cannot detect the consistency of low yields in the final column of the strawberry trial.
“Within-row adjustment cannot detect the consistency of the low yields in the ®nal column.”
Quote in the paperNot a substantial claim - Interpretationreasoning
Heavy shrinkage in the combined analysis confirms that the individual corn trials are not very informative.
“The heavy shrinkage con®rms that the individual trials are not very informative.”
Quote in the paperNot a substantial claim - Interpretationnone given
Expression (2.7) is invariant to the addition of arbitrary row and column effects.
“Clearly, expression (2.7) is invariant to the addition of arbitrary row and column eects.”
Quote in the paperSubstantial claim - Interpretationnone given
When ρ equals 0.5, equation (2.5) is equivalent to locally planar interpolation of fertilities by ordinary least squares.
“When 0:5, equation (2.5) is equivalent to locally planar interpolation of fertilities by the method of ordinary least squares.”
Quote in the paperNot a substantial claim - Interpretationnone given
The accuracy with which treatment or variety effects are estimated depends crucially on how unmeasured fertility patterns are accommodated in the analysis.
“The accuracy with which treatment or variety eects are estimated depends crucially on the means by which these unmeasured patterns are accommodated in the analysis.”
Quote in the paperSubstantial claim - Interpretationnone given
An analysis of positive counts with zeros discarded as missing data would not be legitimate.
“Note that an analysis of the positive counts, with the 0s discarded as `missing data', would not be legitimate.”
Quote in the paperSubstantial claim - Interpretationreasoning
The hierarchical-t formulation for yields and fertilities incorporates an allowance for model uncertainty.
“Thus, the hierarchical-t formulation for yields and fertilities incorporates an allowance for model uncertainty.”
Quote in the paperNot a substantial claim - Interpretationreasoning
The data for the second North Carolina trial must be treated with suspicion.
“But, whatever the true explanation, the data for this trial must be treated with suspicion.”
Quote in the paperNot a substantial claim - Interpretationnone given
With a uniform prior, Bayesian numerical results are expected to match the corresponding frequentist spatial analysis closely.
“Under a uniform prior, the numerical results should agree closely with the corresponding frequentist spatial formulation, as mentioned above, but it seems unfair to remove the shrinkage.”
Quote in the paperNot a substantial claim - Interpretationcitation
Practically, data are scarce at both short and long distances for variogram estimation, as Smith's figures show.
“The behaviour of the true variogram close to the origin and at large distances is highly informative but, in practice, there is a lack of data at both scales, as in Smith's ®gures.”
Quote in the paperSubstantial claim - Interpretationreasoning
If varieties are systematically numbered and an initial block is not randomized, a varietal effect could be confounded with a fertility effect in that block.
“Then, if varieties are systematically numbered, say by parentage or according to a previous ranking, a varietal eect could be confounded with a fertility eect in the initial block.”
Quote in the paperNot a substantial claim - Interpretationnone given
Intrinsic models do not entail a shared mean for fertility effects.
“Harville and Zimmerman also claim incorrectly that intrinsic models imply a common mean for fertility eects but we are grateful to them for pointing out our oversight in the frequentist assessment of prediction errors.”
Quote in the paperNot a substantial claim - Premisereasoning
Likelihood can conflict with the objective of producing good medium- and long-term forecasts in time series.
“There, likelihood, which can be shown to be inextricably linked with one-step-ahead forecasting, may con¯ict with the objective of producing good medium-and long-term forecasts.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Haywood and Tunniclie Wilson (1997) source
- Interpretationnone given
The spatial mixture model is formally equivalent to a block model where allocations of plots to blocks are data dependent.
“Although this is not how we would interpret our model, it is formally equivalent to a block model where the allocations of plots to blocks (i.e.components) are not asserted a priori but are data dependent.”
Quote in the paperNot a substantial claim - Premisecitation
For a first-order autoregression on a rectangle as defined in equation (2.4), the eigenvalues and eigenvectors are available in closed form.
“Second, if W is the precision matrix of a ®rst-order autoregression on a rectangle as de®ned in equation (2.4), then the eigenvalues and eigenvectors are available in closed form; see KuÈ nsch (1994).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Künsch (1994)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationcitation
The different interpretations of the strawberry trial by Mead (1988) and the present authors suggest an unacknowledged double blocking confounding one treatment contrast with half-rows.
“The dierent interpretations of the strawberry trial given by Mead (1988), section 10.0, and the present authors suggest an unacknowledged double blocking confounding one treatment contrast with half-rows (cf.Yates (1939), section 10).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Mead (1988)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Yates (1939)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisereasoning
Process (2.2) corresponds to edge increments that are iid conditional on summing to zero around every circuit of the graph.
“Then expression (2.2) is equivalent to a process whose increments along the edges are independent and identically distributed, conditional on their sums being 0 over all circuits of the graph.”
Quote in the paperNot a substantial claim - Premisecitation
Variogram estimation can suffer conflicts that yield misleading results, as in an agricultural REML example (Littell et al., 1996, section 9.7).
“Also, con¯icts may occur and produce misleading results: for an agricultural example, in which REML is used for estimation, see section 9.7 of Littell et al. (1996).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Littell et al. (1996)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisenone given
The spatial mixture model allows borrowing strength from all plots allocated to the same component, regardless of their location.
“In addition, it allows borrowing strength from all plots allocated to the same component, regardless of their location.”
Quote in the paperSubstantial claim - Premisenone given
In some field trials, a model with high fertility variation, occasional outliers and low residual variance may be as plausible as an alternative with lesser fertility effects, fewer outliers and high residual variance.
“In some examples of ®eld trials it is dicult to settle on a single de®nitive interpretation of the data; for example, a model with high variation in fertility, occasional outliers and a low residual variance may be just as plausible as an alternative model with lesser fertility eects, fewer outliers and a high residual variance.”
Quote in the paperSubstantial claim - Interpretationcitation
An improper posterior is not always fatal.
“It is of course crucial to detect when a posterior distribution is improper but the condition itself is not necessarily fatal: see, for example, the contribution by MacEachern and its implicit reference to Besag et al. (1995).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites MacEachern
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Besag et al. (1995) source
- Interpretationreasoning
Even a proper posterior with finite moments may be poorly estimated by Monte Carlo methods due to extreme tail behaviour in pseudorandom samples.
“Nevertheless, it is possible to have a proper posterior, with ®nite means and variances that are poorly estimated by Monte Carlo methods, because of the freak tail behaviour that can occur in pseudorandom samples.”
Quote in the paperSubstantial claim - Interpretationnone given
In ranking and selection, Bayesian methods avoid the shortcomings of multiple-comparison procedures.
“Preece and Mead rightly criticize us for our reference to multiple-comparison procedures but surely the real issue is that Bayesian methods in ranking and selection do not suer from similar de®ciencies.”
Quote in the paperSubstantial claim - Interpretationreasoning
A strength of MCMC is that medians, credible regions and posterior probabilities are estimated robustly from output, avoiding moment-based asymptotic formulae.
“It is one of the strengths of MCMC methods that such quantities are estimated robustly from the output, without recourse to moment-based asymptotic formulae.”
Quote in the paperSubstantial claim
- Classified as probably not a substantial claim. 58
- May combine two claims. 61
- The quote only partly states this claim. 2
Classified as probably not a substantial claim.
PremisecitationCombined effects of pollinator behaviour, out-crossing rates and pollen pool contribution would seem to bestow an advantage on the white allele.
“However, the combined eects of factors, such as pollinator behaviour, out-crossing rates and the contribution to the pollen pool, would seem to bestow an advantage on the white allele (Rausher and Fry, 1993).”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Rausher and Fry, 1993 source
May combine two claims.
Findingnone givenOne-dimensional fertility adjustment is sufficient for the El Batán example but not for the other three applications.
“One-dimensional fertility adjustment is sucient in this example but not in the other three.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThe posterior probability that P has maximal effect is 0.38; for P or E it is 0.72; for P, E or V it is 0.87.
“The posterior probability that P has maximal eect is 0.38; for either P or E this probability is 0.72, and for P, E or V it is 0.87.”
Quote in the paperSubstantial claimThe quote only partly states this claim.
FindingreasoningThe standard errors for the incomplete-block analyses of the corn trials are noticeably larger than the Bayesian posterior standard deviations.
“The standard errors for the incomplete-block analyses are noticeably larger and this may re¯ect the strong row orientation of the blocks.”
Quote in the paperNot a substantial claimMay combine two claims.
FindingreasoningThe locally quadratic Bayesian fertility model produces the same ranking of strawberry varieties, but its posterior means differ a little because it absorbs arbitrary row and column effects.
“the locally quadratic Bayesian fertility model produces the same ranking of varieties but, because it absorbs arbitrary row and column eects, the posterior means are a little dierent”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataIn the morning-glory seed counts, there are 1277 zeros; non-zero counts range from 1 to 1665, with median 98 and mean 197.
“There are 1277 0s, indicated by white pixels.The non-zero counts range between 1 and 1665, with a median of 98 and mean of 197.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataVariety 1 has a suspiciously large yield in the final replicate and is therefore penalized by the t-analysis.
“Variety 1 has a suspiciously large yield in the ®nal replicate and hence is penalized by the t-analysis.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataVariety 20 has one of the lowest yields in the final replicate but otherwise performs very well, and its mean is enhanced by the t-analysis.
“Variety 20 has one of the lowest yields in the ®nal replicate but otherwise performs very well.Its mean is enhanced by the t-analysis”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataVariety 4 has posterior probability about 0.5 of being best and probability above 0.9 of being among the top five.
“variety 4 has posterior probability about 0:5 of being best and probability in excess of 0:9 of being among the best ®ve.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataFor an 18 by 11 lay-out with ρ=1/2, the ratio of maximum to minimum variance of adjacent plot fertility differences is 1.39 without virtual plots; adding borders of widths 2 and 3 reduces it to 1.02 and 1.01.
“As a numerical example, relevant to Section 6, consider an 18 Â 11 lay-out and suppose that 1 2 .Then, without virtual plots, the ratio of the maximum to the minimum of var i À j for i $ j is 1.39.However, with the inclusion of borders of widths 2 and 3 respectively, the ratio drops to 1.02 and 1.01 for the original pairs of plots”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataUnmeasured plot effects were originally allowed in the morning-glory model but proved negligible and were omitted.
“Originally, we also allowed p to depend on a vector of unmeasured plot eects but these proved to be negligible and were subsequently omitted.”
Quote in the paperNot a substantial claimMay combine two claims.
FindingreasoningThere is a substantial improvement in consistency between individual and combined results when Gaussian rather than uniform variety priors are used, because of additional shrinkage.
“Finally, as we might expect, there is a substantial improvement in consistency between the individual and combined results when Gaussian, rather than uniform, variety priors are used, because of the additional shrinkage.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe t-analysis suggests a fall in fertility in the first replicate between varieties 10 and 11, which demotes variety 10 and promotes variety 11.
“This analysis also suggests a fall in fertility in the ®rst replicate between varieties 10 and 11, which therefore demotes the former and promotes the latter.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThere is an almost linear decrease in fertility from left to right in each row, but it is so slight (0.04-0.06) that the adjustment is negligible.
“There is an almost linear decrease in fertility from left to right in each row but this is so slight (0.04±0.06) that the adjustment is negligible.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataIn the hierarchical-t analysis of the top 15 varieties, posterior means are generally shrunk towards 0 relative to the uniform prior, and this shrinkage is pronounced.
“The means are generally shrunk towards 0 in comparison with corresponding values under the uniform prior.This must always happen but here the eect is quite pronounced”
Quote in the paperNot a substantial claimMay combine two claims.
FindingcitationIndividual 80% credible intervals for P, E and V are (0.29, 1.26), (0.23, 1.26) and (0.04, 1.01), with simultaneous intervals (0.09, 1.45), (0.02, 1.48) and (-0.16, 1.21).
“Individual 80% credible intervals for P, E and V are (0.29, 1.26), (0.23, 1.26) and (0.04, 1.01) respectively, with corresponding simultaneous intervals (0.09, 1.45), (0.02, 1.48) and (À0:16, 1.21)”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe corresponding hierarchical-t analysis rates P best, with V, G and E tying for second, though P is the only variety whose lower 10% point is positive.
“The corresponding hierarchical-t analysis provides some compensation, so that P is rated best, with V, G and E tying for second, though P is the only variety whose lower 10% point is positive.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe corresponding precisions are comparable in the two morning-glory fields, but anisotropy parameters differ considerably, with means 0.74 on the left and 0.39 on the right.
“The corresponding precisions are comparable in the two ®elds but the anisotropy parameters dier considerably, with means 0.74 (left) and 0.39 (right).”
Quote in the paperNot a substantial claim- The authors state this as an assumption: In the morning-glory model, plants produce seeds independently given the centred effects and plot fertilities.
- The authors state this as an assumption: The morning-glory model postulates that seed-production probabilities follow a logistic form with additive centred effects and plot fertilities.
May combine two claims.
Findingown dataIn separate Bayesian analyses of the six corn trials, the posterior mean asymmetry parameter varies between 0.3 and 0.8, except in county 4 where the mode is close to 0, corresponding to independent column adjustment.
“The posterior mean for the asymmetry parameter varies between 0.3 and 0.8, except in county 4, where the mode is close to 0, corresponding to independent column adjustment.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataLimited experience suggests that the continuum formulation is effectively equivalent to the formulation based on Gaussian random walks.
“Limited experience with this formulation suggests that it is eectively equivalent to that based on Gaussian random walks.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataFor the data in Section 3, the continuum formulation is effectively equivalent to the Gaussian random walk formulation.
“For example, this holds for the data in Section 3.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataPosterior fertility means in the morning-glory analysis show a very strong spatial pattern, with seed-production probability ranging from about 0.1 to 0.9 over the layout.
“The posterior fertility means in the lower panel of Fig. 3 show a very strong spatial pattern, as expected, with the probability of seed production ranging from about 0.1 to 0.9 over the layout.”
Quote in the paperNot a substantial claim- The authors state this as an assumption: In the morning-glory model, plants produce seeds independently given the centred effects and plot fertilities.
- The authors state this as an assumption: The morning-glory model postulates that seed-production probabilities follow a logistic form with additive centred effects and plot fertilities.
May combine two claims.
Findingown dataIn county 4 of the corn trials, the geostatistical analysis estimates a very small range of dependence, so fertility corresponds closely to yield and is rather noisy.
“In the geostatistical analysis, the estimated range of dependence is very small, so fertility corresponds closely to yield and is rather noisy.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataShading from a hedge severely delayed crop production in the strawberry trial, making a simple analysis inappropriate.
“As is evident, shading from a hedge severely delayed crop production in its vicinity, so a simple analysis is inappropriate”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe variety effects from the Bayesian and geostatistical analyses of the corn trials are very similar, but those from the incomplete-block analysis differ substantially in places.
“The variety eects for the Bayesian and geostatistical analyses are very similar but those for the incomplete-block analysis dier substantially in places.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThere is no evidence that colour intensity or seed pool has any effect on whether morning-glory plants produce seeds.
“There is no evidence of main eects, either in Fig. 4 or more formally.”
- Also stated in: § 5.1. Results · plain
“Thus, we ®nd no evidence that colour intensity or seed pool have any eect in whether plants produce seeds.”
Quote in the paperSubstantial claim- The authors state this as an assumption: In the morning-glory model, plants produce seeds independently given the centred effects and plot fertilities.
- Rests on an assumption: Morning-glory plants are susceptible to competition through overtopping, but competition is not an issue at the spacing used in this experiment.
- The authors state this as an assumption: The morning-glory model postulates that seed-production probabilities follow a logistic form with additive centred effects and plot fertilities.
- Also stated in: § 5.1. Results · plain
May combine two claims.
FindingreasoningBecause the combined analysis adjusts each variety effect towards a common combined value, the posterior distribution for each variety effect is shifted slightly left and that for each interaction slightly right.
“However, since the combined analysis adjusts each variety eect t k towards a common combined value k , the posterior distribution for each y is shifted slightly to the left and that for each slightly to the right.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataFigs 13(a) and 13(b) indicate a directionally dependent effect for county 1, but Figs 13(c) and 13(d) show no corresponding effect for county 4.
“Figs 13(a) and 13(b) indicate a directionally dependent eect for county 1, but from Figs 13(c) and 13(d) there is no sign of any corresponding eect for county 4, which is very surprising in view of the results in the paper.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingnone givenIn the second North Carolina trial, each of two varieties has its largest yield roughly three times its smallest, with large yields on end plots.
“In the second North Carolina trial, each of two varieties has its largest yield equal to roughly three times its smallest, with the large yields on end plots.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenThe prior distributions for the degrees of freedom of t-distributions for residual errors and fertility are among the more elaborate features of the authors' models.
“The prior distributions for the degrees of freedom of t-distributions for residual errors and fertility comprise one of the more `elaborate' features of the authors' models.”
Quote in the paperNot a substantial claimMay combine two claims.
FindingcitationThe North Carolina trial design is a triple lattice but also has a missing piece in one corner.
“The design is a triple lattice (Cochran and Cox (1957), section 10.22) in the sense of Yates (1937), p. 87, but it is also a jigsaw puzzle design, in the sense of Hamleys of Regent Street's Christmas oers, 1998 Ð `complete' with the deliberately missing piece in one corner!”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Cochran and Cox (1957)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Yates (1937)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims. The quote only partly states this claim.
Findingown dataIn the El Batán trial, the varieties appear in numerical order in block 1, which suggests that block may not have been randomized.
“In Table 1, giving yields for the El Bata n trial, the varieties appear in numerical order for block 1,”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingnone givenThe standard errors of treatment differences in the strawberry experiment lie in the range (0.53, 0.59), which is marginally better than with the Bayesian analysis presented.
“Also, the standard errors of treatment dierences lie in the range (0.53, 0.59), which is marginally better than with the Bayesian analysis presented.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataIn the winter wheat trial, credible interval widths vary by a factor of 2 in one analysis, while those in (c) are almost constant.
“However, the widths of the corresponding credible intervals dier from one another by a factor of 2, whereas those in (c) are nearly constant.”
Quote in the paperNot a substantial claimMay combine two claims.
FindingcitationWith stationary autoregressions, large neighbourhoods are needed but many unexplained structures remain, suggesting non-stationary behaviour.
“With stationary autoregressions we need large neighbourhoods but we still ®nd many unexplained structures in the data, which suggest various kinds of non-stationary behaviour; see KuÈ nsch (1982).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Künsch (1982)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenThe El Batán trial used complete blocks of 50 plots.
“The El Bata n trial used complete blocks of 50 plots, even though 50 is evidently too large for the assumption of within-block homogeneity.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataIn the winter wheat trial, analysis (b) shows negligible shrinkage of variety effects, as the data offer no support for it.
“Note that the shrinkage of the variety eects in (b) is negligible because there is no support for it in the data.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataA simple hierarchical-t analysis of the data suggests perhaps three breaks in fertility but no outliers and produces an average posterior standard deviation of variety differences marginally less than the corresponding standard errors in Gilmour et al. (1997).
“A simple hierarchical-t analysis of the data suggests perhaps three breaks in fertility but no outliers and produces an average posterior standard deviation of variety dierences that is marginally less than the corresponding standard errors in Gilmour et al. (1997).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Gilmour et al., 1997 source
May combine two claims.
Findingown dataAmong the simulations, the visible evidence for non-stationarity is not clear, and for ρ=0.95 there is not much evidence for directional dependence despite strong directionality in the model.
“Among the simulations, the visible evidence for non-stationarity is not at all clear, and for 0:95 in Figs 13(k) and 13(l) there is not much evidence for directional dependence either, despite the strong directionality in the model.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingnone givenA uniform variety prior yields a defective overall posterior, though variety contrasts generally have a proper distribution.
“Indeed, the uniform variety prior, which we again emphasize was used purely for numerical comparison, produces a defective overall posterior but in general a proper distribution for variety contrasts.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataFig. 5's fertility map indicates that the aberrant row in county 2 is absorbed and that variety effect estimates are largely unaffected.
“The fertility map in Fig. 5 suggests that the aberration is successfully absorbed and that the estimates of variety eects are not much aected.”
Quote in the paperNot a substantial claimMay combine two claims.
FindingcitationFor intrinsic autoregression (2.5)-(2.6), the true variogram grows roughly logarithmically with distance, matching empirical findings of Fairfield Smith (1938) and Pearce (1976).
“Note that, for the intrinsic autoregression (2.5)±(2.6) in Smith's simulations, the true variogram increases approximately as the logarithm of distance (Besag, 1981;Besag and Kooperberg, 1995), which is in close agreement with the empirical results claimed by Fair®eld Smith (1938) and Pearce (1976).”
Quote in the paperNot a substantial claim0 of 3 sources checkedMay combine two claims.
Findingown dataPosterior expected loss is about 8% for variety 4 in the El Batán trial; in the spring barley trial, it is about 1% for variety 7 and under 10% for eight others.
“Thus, in the El Bata n trial, the posterior expected loss for variety 4 is about 8%, whereas, in the spring barley trial discussed earlier in the reply, it is about 1% for variety 7 and less than about 10% for eight other varieties.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
Interpretationnone givenUsing Bayesian methods makes the interpretation of results simpler, particularly for ranking and selection.
“Adoption of the Bayesian paradigm simpli®es the interpretation of the results, especially in ranking and selection.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Interpretationnone givenComplex model formulations can be analysed relatively easily with Markov chain Monte Carlo methods.
“Also, complex formulations can be analysed with comparative ease, by using Markov chain Monte Carlo methods.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationSuch random walks epitomize non-stationarity in the prior distributions and were first adopted by Besag and Kempton (1986) in frequentist spatial analysis.
“Such random walks epitomize the non-stationarity in our prior distributions and were ®rst adopted by Besag and Kempton (1986) in frequentist spatial analysis.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Besag and Kempton, 1986 source
Probably not a substantial claim: may not pass S4 (based).
Premisenone givenIf random samples of sizes n and 3n are taken from two independent N(0, sigma) populations, the probability that the n-sample mean is larger in magnitude is 2/3.
“If random samples of sizes n and 3n are taken from two independent N0, populations, there is probability 2 3 that the n-sample mean will be the larger in magnitude.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenFor hierarchical-t random walks, blocking ω and λ provides a significant computational improvement.
“Blocking of ! and again provides a signi®cant improvement computationally”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningAs the asymmetry parameter tends to its extremal values, the model yields random walks along rows or columns, which are used for one-dimensional fertility adjustment.
“Also, as the asymmetry parameter P 0, 1 tends to each of its extremal values, we obtain random walks along the rows or along the columns of the trial and these we employ for one-dimensional fertility adjustment.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationInformation distributed about the corn trials includes the statement that growers are cautioned against making varietal selection decisions based on an individual location in any one year.
“The information distributed about such trials includes the statement `Growers are cautioned against making varietal selection decisions based on an individual location in any one year' (Bowman, 1995).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Bowman, 1995
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
InterpretationreasoningIn either column random walk or locally quadratic fertility modelling, there is a danger of spurious overfitting to some columns because each column has few observations; this danger does not occur with the first-order two-dimensional fertility model.
“In either case, there is the danger of spurious over®tting to some columns, since the number of observations in each is so small.This danger does not occur with the ®rst-order two-dimensional fertility model.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone givenThe four-neighbour Gaussian intrinsic autoregression adopted as the base-line representation of two-dimensional fertility variation involves two unknown parameters controlling scale and lateral asymmetry.
“Thus, we have adopted a four-neighbour Gaussian intrinsic autoregression as our base-line representation of two-dimensional variation in fertility.This particular choice involves two unknown parameters, controlling scale and lateral asymmetry”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Premisenone givenContinuum models are self-consistent as plot size varies.
“Of course, the main attraction of continuum models is that they are self-consistent as plot size varies.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone givenThe conclusion of no main effects in the morning-glory experiment would be strengthened if the uniform treatment prior were replaced by a Gaussian distribution.
“Our conclusion would only be strengthened if the uniform treatment prior were to be replaced by a Gaussian distribution.”
Quote in the paperNot a substantial claim- The authors state this as an assumption: In the morning-glory model, plants produce seeds independently given the centred effects and plot fertilities.
- Rests on an assumption: Morning-glory plants are susceptible to competition through overtopping, but competition is not an issue at the spacing used in this experiment.
- The authors state this as an assumption: The morning-glory model postulates that seed-production probabilities follow a logistic form with additive centred effects and plot fertilities.
May combine two claims.
PremisecitationThe four-neighbour Gaussian intrinsic autoregression has dual interpretations as a limiting version of a first-order stationary Gaussian conditional autoregression and as locally planar stochastic interpolation.
“and has dual interpretations, both as a limiting version of a ®rst-order stationary Gaussian (conditional) autoregression (Besag, 1974) and as locally planar stochastic interpolation.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Besag, 1974 source
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenThe order of updating λ_y and β is crucial.
“Note that here the order of updating y and is crucial.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductcitationThe geostatistical formulation does not allow for uncertainty in estimating the parameters in expression (6.1), though this could be corrected in a fully Bayesian analysis.
“Finally, note that the geostatistical formulation does not allow for uncertainty in estimating the parameters in expression (6.1), though this could be corrected in a corresponding fully Bayesian analysis (Diggle et al., 1998).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Diggle et al., 1998
May combine two claims.
InterpretationreasoningThe authors considered such differencing unsuitable in a prior distribution because it absorbs arbitrary row and column effects and is therefore highly degenerate.
“There, we considered such dierencing unsuitable in a prior distribution because it absorbs arbitrary row and column eects and is therefore highly degenerate.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationreasoningt-distributions provide an exception to the usual posterior complications, allowing a simple Gibbs sampler.
“Although non-Gaussian components in the prior and in the likelihood generally lead to complications in the posterior that are best handled via Hastings rather than Gibbs steps, t-distributions provide an exception.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationGilmour et al. (1997) reached an analogous frequentist conclusion that double differencing is inefficient because it loses too many degrees of freedom.
“Gilmour et al. (1997) reached an analogous frequentist conclusion that double dierencing is inecient because it loses too many degrees of freedom.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Gilmour et al., 1997 source
May combine two claims.
InterpretationreasoningIn a Gaussian formulation, a few anomalies spread uncertainty among all effects, whereas the t-formulation anticipates occasional aberrations.
“In a Gaussian formulation, the existence of a few anomalies spreads uncertainty among all eects, whereas the t-formulation anticipates occasional aberrations.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone givenThe resulting analysis is closely related to frequentist methods developed by Cullis and Gleeson (1991) and investigated empirically by Kempton et al. (1994).
“The resulting analysis has close ties with the frequentist methods developed by Cullis and Gleeson (1991) and investigated empirically by Kempton et al. (1994).”
Quote in the paperNot a substantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone givenThe order of updating ω and λ is again important.
“so that order is again important.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningWhen ρ equals 1, the model yields independent random walks along the columns, and when ρ equals 0, along the rows.
“When 1 in equation (2.5), we obtain a set of independent random walks along the columns of the array, each with an N0, 1= increment distribution, and similarly along the rows if 0.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone givenThe device of adding layers can also be applied to non-Gaussian formulations for fertility.
“The device of adding layers can also be applied to non-Gaussian formulations for but, in the Gaussian case, there are simpli®cations in the evaluation of the eigenvalues of W that can be used to speed up the computations and hence introduce a continuous prior for ; see Section 2.5.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationIn natural populations of morning-glory in south-eastern USA, the W-locus is generally polymorphic, with at least two pigment intensities present and white allele frequency typically below 40%.
“In natural populations in south-eastern USA, the W-locus is generally polymorphic, so at least two of the pigment intensities are present, with the frequency of the white allele being typically less than 40% (Epperson and Clegg, 1988).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Epperson and Clegg, 1988 source
May combine two claims.
PremisecitationThe papers on lattice-based fertility models all support parsimony but differ in their detailed conclusions; the first two favour differencing and non-stationarity, while the third promotes the stationary AR(1) x AR(1) process.
“The papers all support parsimony, with which we concur, but dier in their detailed conclusions.Thus, the ®rst two favour dierencing as a component and hence non-stationarity but the third promotes the (stationary) AR(1) Â AR(1) or doubly geometric process”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Martin, 1979
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Martin, 1990 source
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenUnusually small posterior means of ω_ij suggest the locations of fertility jumps.
“also !ij s with unusually small posterior means suggest the locations of fertility jumps.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone givenFor locally quadratic interpolation, restricting the infinite lattice process to a q1 by q2 array is straightforward.
“Curiously, the restriction of the in®nite lattice process to a q 1 Â q 2 array is now straightforward, with j G exp”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningFor variety 20, the most notable change under the t-analysis is an increase in the width of its credible interval, which indicates model uncertainty.
“but the most notable change is the increase in the width of its credible interval, which is an indication of model uncertainty.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone givenBlocking with λ_y can yield a significant computational improvement.
“Moreover, a signi®cant computational improvement can be made by blocking with y , as below.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone givenThe larger standard errors for the incomplete-block analyses may reflect the strong row orientation of the blocks.
“The standard errors for the incomplete-block analyses are noticeably larger and this may re¯ect the strong row orientation of the blocks.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisereasoningIn the spatial mixture model for El Batán, the prior probability that two plots are allocated to the same component decreases as they become further apart.
“Within each column, the use of a limiting case of a logistic autonormal multinomial allocation scheme, controlled by a smoothing parameter h > 0, ensures that the prior probability that two plots are allocated to the same component decreases as they become further”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenIn well-designed experiments, the benefits of Bayesian analysis will be reduced and may disappear completely.
“Presumably in welldesigned experiments the bene®ts of the Bayesian analysis will be reduced and may disappear completely.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Premisenone givenIn hierarchical modelling, the data may carry little information about hyperparameters, so the usual assumption that prior input is swamped by the data likelihood may be unjustified.
“Unfortunately, it is quite possible that the data will carry little information about such hyperparameters and the usual assumption that the prior input will be swamped by the data likelihood may be unjusti®ed.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningWavelet methods would allow the priors in Section 2.3 to be generalized to allow for intermittent effects.
“Wavelet methods would allow the priors in Section 2.3 of the paper to be generalized, to allow for such intermittent eects; for some discussion of the range of possible behaviour of functions generated from priors over wavelet coecients see, for example, Abramovich et al. (1998).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Abramovich et al. (1998) source
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationIf Z1 and Z2 are independent white noises on Z2, then conditionally on the sums over all loops being zero, they have the same second moments as the increments of a first-order intrinsic autoregression.
“This is indeed true: if Z 1 and Z 2 are two independent white noises on Z 2 , then, conditionally on the sums over all loops being 0, Z 1 , Z 2 has the same second moments as the increments of a ®rst-order intrinsic autoregression.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Priestley (1981)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
Interpretationnone givenThe vast majority of intrinsic autoregressions do not suffer from severe degeneracy, including the one proposed, which has a single desirable rank deficiency producing non-stationarity.
“However, the vast majority of intrinsic autoregressions do not suer from severe degeneracy and this includes the one that we propose, with its single (desirable) de®ciency in rank producing non-stationarity, as in one-dimensional dierencing.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationIntrinsic autoregressions serve as limiting cases of stationary models and can be interpreted as local smoothers.
“These are not only the limiting forms of corresponding stationary models but also have an interpretation as local smoothers, as noted by Ferna ndez and Green.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Fernández and Green
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenRepeated ploughing over a very long period would reduce local variations in fertility.
“One might expect that repeated ploughing over a very long period would reduce the local variations in fertility.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Premisenone givenThe practical simplicity of Fisher's scheme of design and analysis of agricultural field experiments was quickly appreciated worldwide.
“It is a historical fact that the practical simplicity of Fisher's scheme of design and analysis of agricultural ®eld experiments was quickly appreciated worldwide.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisereasoningOn the basis of the Wiener process assumption, Jones could make a considerable reduction in the errors in contrasts.
“On the basis of this assumption Jones could make a considerable reduction in the errors in contrasts.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone given50 plots is too large for the assumption of within-block homogeneity.
“The El Bata n trial used complete blocks of 50 plots, even though 50 is evidently too large for the assumption of within-block homogeneity.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
PremisereasoningBean yields were affected by worms in the soil whose breeding habits suggested that fertility along a row could be represented by a Wiener process with positive autocorrelation.
“It was known that the bean yields were aected by worms in the soil whose breeding habits suggested that fertility along a row could reasonably be represented by a Wiener process with positive autocorrelation.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Premisenone givenFisher's simplicity was linked with his identification of the analysis with the design.
“This simplicity was linked with Fisher's identi®cation of the analysis with the design.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Premisenone givenA random walk is represented by a linear graph with no constraints.
“Note that a random walk corresponds to a linear graph and the absence of any constraints.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Premisenone givenDr A. E. Jones studied fertility variations of rows of bean plants at Rothamsted.
“Dr A. E.Jones, who was killed in a lift accident in 1948, studied fertility variations of rows of bean plants at Rothamsted.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationTraditional analysis of covariance requires a regression coefficient below 1; its adjustment is attenuated and resembles stationarity rather than being strictly local.
“Note that, in the traditional analysis of covariance, the regression coecient must be less than 1 and the corresponding adjustment is not strictly local as claimed by Pearce (1998) and here in the discussion but is attenuated and more akin to that obtained under stationarity.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Pearce (1998) source
May combine two claims.
Interpretationnone givenA Gaussian prior for variety effects makes Bayesian analysis non-additive; its shrinkage produces near-perfect results that do not generalize.
“However, Bayesian analysis, with a Gaussian prior for variety eects, is non-additive and shrinkage will produce results that are close to perfect but that do not generalize.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataThe variogram results probably support the use of a non-stationary model, but the evidence is not strong.
“If these results show anything, they probably support the use of a non-stationary model, but the Downloaded from https://academic.oup.com/jrsssb/article/61/4/691/7083194 by guest on 19 December 2023 evidence is not strong.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.
Premisenone givenPractitioners are reluctant to implement either new designs or new methods of analysis.
“In my experience, practitioners are reluctant to implement either new designs or new methods of analysis.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisereasoningAttention to short-range soil variation has grown considerably since field experimentation extended to regions such as Australia and North America, where land has been under plough for shorter periods than at Rothamsted.
“I think that it is true that attention to short-range soil variation has grown considerably since ®eld experimentation has been extended to regions such as Australia and much of North America, where the land involved has been under the plough for much shorter periods than the land at Rothamsted.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Premisenone givenNull results in small samples sometimes misled an experimenter into believing that no real effects existed.
“In particular, `null' results in small samples sometimes misled an experimenter into believing that no real eects existed.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Premisenone givenFertility is intrinsically an intermittent phenomenon.
“Fertility is intrinsically an intermittent phenomenon.”
Quote in the paperNot a substantial claim- The authors state this as an assumption: Fertility is intrinsically an intermittent phenomenon.
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
PremisecitationBayesians who claim that their approach covers all inference ignore the rejection of their approach by many scientists and philosophers.
“Bayesians who claim that their approach covers all inference ignore the rejection of their approach by many scientists and philosophers (see, for example, Mayo (1996)).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Mayo (1996) source
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Premisenone givenThe spatial mixture methodology is directly applicable to arbitrary graphs.
“Although here we apply a one-dimensional adjustment, our methodology is directly applicable to arbitrary graphs.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.
Study design and conductnone givenThe choice of hyperpriors is rather arbitrary and the rescaling of the data is inelegant, though neither has produced practical problems in analyses of many data sets.
“Among many other topics given scant attention, we note the rather arbitrary choice of hyperpriors and the inelegant rescaling of the data, though neither has produced any practical problems in the analysis of many dierent data sets, so far as we know.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone givenThe result can be extended so that non-zero ωij values may differ.
“An obvious extension permits the non-zero !ij s to be unequal.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationReplacing the squares in equation (2.4) by a Huber function can preserve sharp edges without increasing computational difficulty too much.
“In KuÈ nsch (1994) I also showed that replacing the squares in equation (2.4) by a Huber function can preserve sharp edges without increasing the computational diculty too much.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Künsch (1994)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing).
Premisenone givenIn one dimension, a first-order intrinsic autoregression means that the first differences are white noise.
“For instance, in one dimension, a ®rst-order intrinsic autoregression simply means that the ®rst dierences are white noise.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Premisenone givenUsing t increments preserves a useful Gaussian interpretation for any specified degrees of freedom.
“In fact, the switch to t increments retains a useful Gaussian interpretation for any given , as noted in Section 2.5.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningThe authors' approach already incorporates some model uncertainty, both in the basic formulation where values close to extremes produce essentially one-dimensional adjustment, and in the hierarchical-t representation through variation in degrees of freedom.
“However, our approach already incorporates some model uncertainty, both in the basic formulation (2.8), where values of close to its extremes produce essentially one-dimensional adjustment, and in the hierarchical-t representation, through variation in the degrees of freedom.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.
Premisenone givenRandomization has some problems for frequentists, notably in small samples, but its role for a Bayesian is much less clear.
“Randomization has some problems for frequentists, notably in small samples, but its role for a Bayesian is much less clear.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Premisenone givenErrors in copying and arithmetic are all too frequent in agricultural and horticultural trial data.
“Errors in copying and in arithmetic are all too frequent.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
PremisereasoningIt is extremely difficult to find simple models that give a good fit to uniformity trials.
“Finally, I would like to con®rm that it is extremely dicult to ®nd simple models which give a good ®t to uniformity trials.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Premisenone givenIn other areas of statistical research where stochastic processes are used, likelihood-based methods of inference for non-location parameters do not always perform well.
“Certainly, in other areas of statistical research where stochastic processes are used, likelihood-based methods of inference for non-location parameters do not always perform well.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenEven with Bayesian analysis, a well-designed experiment will still perform better than a badly designed experiment.
“However, even with the Bayesian analysis, a well-designed experiment will still perform better than a badly designed experiment.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationDirect variogram fitting lacks an explicit class of allowable functions, and invalid choices sometimes occur.
“In direct ®tting, which is often employed for geological data, the class of allowable variogram functions is not known explicitly and sometimes an invalid choice is made, as discussed by Gneiting (1996).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Gneiting (1996)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenA benefit of spatial analysis is that special-purpose design criteria affect its efficiency less.
“One advantage of spatial analysis is that its eciency is less aected by special purpose design criteria.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningBecause the analysis is non-additive, its results do not generalize.
“Such examples were omitted from the paper because they are somewhat ambiguous: the Gaussian prior for variety eects provides desirable shrinkage, also advocated by Patterson in his contribution, but the non-additivity of the analysis implies that the results do not generalize.”
Quote in the paperNot a substantial claim
The paper relies on this without support.
Bayesian analysis for agricultural field experiments has received very little previous attention.
“The paper describes Bayesian analysis for agricultural ®eld experiments, a topic that has received very little previous attention, despite a vast frequentist literature.”
The paper relies on this without support.
Generally, a crop performs much more similarly on two experimental units that are close together than on plots that are further apart.
“Field trials are rather dierent, for it has always been recognized that generally a crop will perform much more similarly on two experimental units (plots) that are close together than on plots that are further apart.”
The paper relies on this without support.
Variogram estimation is unreliable in small data sets.
“In comparison, variogram estimation is unreliable in small data sets.”
The authors state this as an assumption.
In the morning-glory model, plants produce seeds independently given the centred effects and plot fertilities.
“The likelihood follows on assuming that plants produce seeds independently, given and .”
The paper relies on this without support.
Morning-glory plants are susceptible to competition through overtopping, but competition is not an issue at the spacing used in this experiment.
“We note that morning-glory plants are susceptible to competition through overtopping but this is not an issue at the spacing in this experiment.”
The authors state this as an assumption.
The morning-glory model postulates that seed-production probabilities follow a logistic form with additive centred effects and plot fertilities.
“Then, our model postulates that the corresponding vector p is of the form p expF T 1 expF T , in the same notation as equation (2.1), where exponentiation and division on the right-hand side occur element by element.”
The paper relies on this without support.
The hedge in the strawberry experiment diminishes the yields of plots adjacent to it.
“The hedge is thought to be diminishing the yields of plots adjacent to it.”
The paper relies on this without support.
The practice of not randomizing an initial block has sometimes been recommended and adopted.
“Alas, the practice of not randomizing an initial block has sometimes been recommended and adopted.”
The authors state this as an assumption.
Fertility is intrinsically an intermittent phenomenon.
“Fertility is intrinsically an intermittent phenomenon.”
The authors state this as an assumption.
The design spreads treatments widely enough to limit confounding with spatial effects, and the spatial model is not so complex that it absorbs real treatment effects.
“Note the implicit assumption here that treatments are spread out suciently by the design to minimize confounding with spatial eects, and, correspondingly, that the spatial formulation is not so elaborate as to absorb genuine treatment eects.”
The paper relies on this without support.
Poorly designed experiments occur frequently in practice.
“Nevertheless, badly designed experiments are common in practice and statisticians need to cope with them in the best possible way, not merely to encourage better designs in the future.”
The paper relies on this without support.
Counties 2 and 3 faced a spring drought and heavy rainfall in July and August.
“counties 2 and 3 were subject to a drought during the spring and to heavy rains in July and August.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The hierarchical regression analysis assumes exchangeable effects within batches, which is not necessarily best in every application; spatial models may be preferable for some data.
“The hierarchical regression analysis is based on the model of exchangeable effects within batches, as expressed in model (7), which is not necessarily the best analysis in any particular application.For example, Besag and Higdon (1999) recommend using spatial models (rather than exchangeable row and column effects) for data such as in the split-plot experiment described previously.”
From Analysis of variance—why it is more important than ever (2005) · cites this paper as Besag and Higdon (1999)
Besag and Higdon develop spatial models for agricultural field experiments and say that a model's invariance to arbitrary row and column effects is undesirable (section 2.3.2, page unavailable). This supports the general preference for spatial modelling, but the passages do not specifically recommend it over exchangeable row and column effects for the split-plot experiment. The statement about the citing authors’ hierarchical regression model is not attributed to Besag and Higdon. (LLM, conf=0.78)
Experimental — accuracy not yet measuredMore complex structures such as time-series and spatial models, or negative intraclass correlations, cannot be additively decomposed into exchangeable components.
“More complex structures, such as time-series and spatial models [see Ripley (1981) and Besag and Higdon (1999)], or negative intraclass correlations, cannot be additively decomposed in a natural way into exchangeable components.”
From Analysis of variance—why it is more important than ever (2005) · cites this paper as Besag and Higdon (1999)
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Gilmour, A. R., Cullis, B. R., Frensham, A. B., & Thompson, R. (1998) · COMPSTAT · doi:10.1007/978-3-662-01131-7_5 · Cited by 6 · see citing works
CONTINUATION OF DR JONES'S PAPER
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A Subclass of Lattice Processes Applied to a Problem in Planar Sampling
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Systematic Sampling of Continuous Parameter Populations
Jones, A. E. (1948) · Biometrika · doi:10.2307/2332348 · Cited by 3 · see citing works
Continuation of Dr Jones's Paper
Kendall, M. G. (1948) · Biometrika · doi:10.2307/2332349 · Cited by 3 · see citing works
Interplot competition in cereal variety trials
Philippeau, G., David, O., & Monod, H. (1996) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 2 · see citing works
The agricultural field experiment. A statistical examination of theory and practice
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A frequency domain comparison of estimation methods for gaussian fields
Butler, N. (1998) · Communications in Statistics - Theory and Methods · doi:10.1080/03610929808832230
Analysis of Agricultural Field Trials in the Presence of Outliers and Fertility Jumps
Taplin, R. H., & Raftery, A. E. (1994) · Biometrics · doi:10.2307/2532790
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Cited by
Sorted by citations
Generalized additive models for location, scale and shape.
Rigby, R. A., & Stasinopoulos, D. M. (2005) · Journal of the Royal Statistical Society Series C: Applied Statistics · doi:10.1111/j.1467-9876.2005.00510.x · Cited by 3,228 · see citing works
“Besag et al. (1991) and Besag and Higdon (1999) considered models for spatial random effects with singular multivariate normal distributions, whereas Breslow and Clayton (1993), Lee and Nelder (2001b) and Fahrmeir and Lang (2001) considered incorporating these spatial terms in the predictor of the…”
BackgroundLang, S., & Brezger, A. (2004) · Journal of Computational and Graphical Statistics · doi:10.1198/1061860043010 · Cited by 888 · see citing works
“Such a prior has been already used in the context of dynamic models (Knorr-Held 1999) and for edge preserving spatial smoothing (e.g., Besag and Higdon 1999).”
MethodQuantile regression: applications and current research areas
Yu, K., Lu, Z., & Stander, J. (2003) · Journal of the Royal Statistical Society Series D (The Statistician) · doi:10.1111/1467-9884.00363 · Cited by 697 · see citing works
“Bayesian quantile regression The use of Bayesian inference for generalized linear models is quite standard these days; see Besag and Higdon (1999). Two obvious advantages of the Bayesian approach over classical inference are that the Bayesian approach may lead to exact inference as opposed to the asymptotic inference…”
MethodInfluentialBeckage, B., Osborne, B. B., Gavin, D. G., Pucko, C. A., Siccama, T. G., & Perkins, T. D. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0708921105 · Cited by 500 · see citing works
Fast Sampling of Gaussian Markov Random Fields
Rue, H. (2001) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/1467-9868.00288 · Cited by 363 · see citing works
Correcting for spatial heterogeneity in plant breeding experiments with P-splines
Rodríguez‐Álvarez, M. X., Boer, M. P., van Eeuwijk, F. A., & Eilers, P. H. C. (2017) · Spatial Statistics · doi:10.1016/j.spasta.2017.10.003 · Cited by 324 · see citing works
“(1997), the separable linear variance model discussed in Piepho and Williams (2010), or the Bayesian model with a prior based on first differences along rows and columns model (Besag and Higdon, 1999). In contrast, smoothing methods model spatial trends explicitly. Their use in the agricultural context dates back to G…”
BackgroundMethodResultInfluentialCARBayes : An R Package for Bayesian Spatial Modeling with Conditional Autoregressive Priors
Lee, D. (2013) · Journal of Statistical Software · doi:10.18637/jss.v055.i13 · Cited by 298 · see citing works
A comparison of conditional autoregressive models used in Bayesian disease mapping
Lee, D. (2011) · Spatial and Spatio-temporal Epidemiology · doi:10.1016/j.sste.2011.03.001 · Cited by 284 · see citing works
McCullagh, P. (2002) · The Annals of Statistics · doi:10.1214/aos/1035844977 · Cited by 248 · see citing works
Double Hierarchical Generalized Linear Models (With Discussion)
Lee, Y., & Nelder, J. A. (2006) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/j.1467-9876.2006.00538.x · Cited by 202 · see citing works
“The state space models of Harvey (1989) and Durbin and Koopman (2000) and the spatial models of Besag and Higdon (1999) are models in which L.ρ/=L, i.e. there are no unknown parameters.”
MethodOn Block Updating in Markov Random Field Models for Disease Mapping
Knorr‐Held, L., & Rue, H. (2002) · Scandinavian Journal of Statistics · doi:10.1111/1467-9469.00308 · Cited by 182 · see citing works
“…block algorithms are not restricted to Gaussian MRF's; the adoption of scale mixtures of normals allows for many other distributional assumptions, including the popular t-distributions, possibly even with an unknown number of degrees of freedom, see Besag et al. (1995) and Besag and Higdon (1999).”
BackgroundMethodLee, D., Rushworth, A., & Napier, G. (2018) · Journal of Statistical Software · doi:10.18637/jss.v084.i09 · Cited by 159 · see citing works
“Examples include the total yield in sectors in an agricultural field trial (Besag and Higdon 1999), the proportion of people who are Catholic in lower super output areas in Northern Ireland (Lee, Minton, and Pryce 2015), the average score on SAT college entrance exams across US states (Wall 2004), and the total number…”
BackgroundInfluentialChildhood Malaria in the Gambia: A Case-Study in Model-Based Geostatistics
Diggle, P. J., Moyeed, R. A., Rowlingson, B., et al. (2002) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/1467-9876.00283 · Cited by 135 · see citing works
“(1991) and Besag and Higdon (1999). One particular issue which arises with model (2) is whether both the Ui- and the S.”
BackgroundBayesian Analysis: A Look at Today and Thoughts of Tomorrow
Berger, J. O. (2000) · Journal of the American Statistical Association · doi:10.1080/01621459.2000.10474328 · Cited by 113 · see citing works
Benoit, D. F., & den Poel, D. V. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.01.031 · Cited by 113 · see citing works
“The use of Bayesian inference for generalized linear models is quite standard these days [2].”
Bayesian Robust Inference for Differential Gene Expression in Microarrays with Multiple Samples
Gottardo, R., Raftery, A. E., Yeung, K. Y., & Bumgarner, R. E. (2005) · Biometrics · doi:10.1111/j.1541-0420.2005.00397.x · Cited by 94 · see citing works
“Robustness is achieved by using a hierarchical-t formulation (Besag and Higdon 1999), which is more robust to outliers than the usual Gaussian model.”
Analysis of variance—why it is more important than ever
Gelman, A. (2005) · The Annals of Statistics · doi:10.1214/009053604000001048 · Cited by 861 · see citing works
“For example, Besag and Higdon (1999) recommend using spatial models (rather than exchangeable row and column effects) for data such as in the split-plot experiment described above.”
BackgroundSpace-varying regression models: specifications and simulation
Gamerman, D., Moreira, A. R. B., & Rue, H. (2003) · Computational Statistics & Data Analysis · doi:10.1016/s0167-9473(02)00211-6 · Cited by 93 · see citing works
Enhanced diagnostics for the spatial analysis of field trials
Stefanova, K. T., Smith, A. B., & Cullis, B. R. (2009) · Journal of Agricultural, Biological, and Environmental Statistics · doi:10.1198/jabes.2009.07098 · Cited by 88 · see citing works
Spline Adaptation in Extended Linear Models (with comments and a rejoinder by the authors
Hansen, M. H., & Kooperberg, C. L. (2002) · Statistical Science · doi:10.1214/ss/1023798997 · Cited by 89 · see citing works
Pettitt, A. N., Weir, I. S., & Hart, A. G. (2002) · Statistics and Computing · doi:10.1023/a:1020792130229 · Cited by 85 · see citing works
On the Possibility of a Private Crop Insurance Market: A Spatial Statistics Approach
Wang, H. H., & Zhang, H. (2003) · Journal of Risk & Insurance · doi:10.1111/1539-6975.00051 · Cited by 81 · see citing works
Markov Chain Monte Carlo for Statistical Inference
Besag, J. E. (2002) · Cited by 79 · see citing works
Self-citationNearest Neighbour Adjustment and Linear Variance Models in Plant Breeding Trials
Piepho, H., Richter, C., & Williams, E. R. (2008) · Biometrical Journal · doi:10.1002/bimj.200710414 · Cited by 70 · see citing works
“Similarly, Papadakis himself remarked that results where b is very different from 1 are suspect (Besag and Higdon, 1999).”
BackgroundMethodOn the Precision of the Conditionally Autoregressive Prior in Spatial Models
Hodges, J. S., Carlin, B. P., & Fan, Q. (2003) · Biometrics · doi:10.1111/1541-0420.00038 · Cited by 74 · see citing works
Locally Adaptive Spatial Smoothing Using Conditional Auto-Regressive Models
Lee, D., & Mitchell, R. (2013) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/rssc.12009 · Cited by 71 · see citing works
“Data arising from non-overlapping spatial units are prevalent in many fields, including agriculture (Besag and Higdon, 1999), epidemiology (Lee, 2011) and image analysis (Gavin and Jennison, 1997). In epidemiology the spatial units typically form an irregular lattice, such as the set of electoral wards in a city or co…”
BackgroundInfluentialOn the relationship between conditional (CAR) and simultaneous (SAR) autoregressive models
Hoef, J. M. V., Hanks, E. M., & Hooten, M. B. (2018) · Spatial Statistics · doi:10.1016/j.spasta.2018.04.006 · Cited by 2 · see citing works
“, Clayton and Kaldor, 1987; Lawson, 2013), agriculture (Cullis and Gleeson, 1991; Besag and Higdon, 1999), econometrics (Anselin, 1988; LeSage and Pace, 2009), ecology (Lichstein et al.”
BackgroundLinear variance models for plant breeding trials
Piepho, H., & Williams, E. R. (2009) · Plant Breeding · doi:10.1111/j.1439-0523.2009.01654.x · Cited by 59 · see citing works
Bayesian change‐point analyses in ecology
Beckage, B., Joseph, L., Bélisle, P., et al. (2007) · New Phytologist · doi:10.1111/j.1469-8137.2007.01991.x · Cited by 62 · see citing works
“The change-point model remains identical to that above, but we now allow correlations between seedling counts in adjacent quadrats by including a Gaussian Markov random field (GMRF) prior for random effects on the λ across quadrats within a transect (Besag & Kooperberg, 1995; Besag & Higdon, 1999).”
MethodA Spatial Analysis of Basketball Shot Chart Data
Reich, B. J., Hodges, J. S., Carlin, B. P., et al. (2006) · The American Statistician · doi:10.1198/000313006x90305 · Cited by 62 · see citing works
“This two neighbor relation CAR (2NRCAR) distribution was mentioned by Besag and Higdon (1999) and developed by Reich, Hodges, and Carlin (2004).”
MethodConditionally specified distributions: an introduction
Arnold, B. C., Ron, E. C., & Sarabia, J. M. (2001) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 62 · see citing works
Hierarchical and Joint Site‐Edge Methods for Medicare Hospice Service Region Boundary Analysis
Ma, H., Carlin, B. P., & Banerjee, S. (2009) · Biometrics · doi:10.1111/j.1541-0420.2009.01291.x · Cited by 56 · see citing works
First-order intrinsic autoregressions and the de Wijs process
Besag, J. E., & Mondal, D. (2005) · Biometrika · doi:10.1093/biomet/92.4.909 · Cited by 56 · see citing works
Self-citationBayesian Modeling of Heterogeneous Error and Genotype × Environment Interaction Variances
Edwards, J. W., & Jannink, J. (2006) · Crop Science · doi:10.2135/cropsci2005.0164 · Cited by 53 · see citing works
“…have been advocated for addressing other issues in multienvironment yield trials such as use of environment specific covariates (Theobald et al., 2002), modeling of multiplicative genotype 3 environment interactions (Viele and Srinivasan, 2000) and spatial analysis (Besag and Higdon, 1999).”
BackgroundSu, Z., Peterman, R. M., & Haeseker, S. L. (2004) · Canadian Journal of Fisheries and Aquatic Sciences · doi:10.1139/f04-168 · Cited by 53 · see citing works
“Here, | |Hγ denotes the product of only the non-zero eigenvalues of Hγ (Besag and Higdon 1999).”
BackgroundInfluentialSpatial Disease Mapping Using Directed Acyclic Graph Auto-Regressive (DAGAR) Models
Datta, A., Banerjee, S., Hodges, J. S., et al. (2019) · Bayesian Analysis · doi:10.1214/19-ba1177 · Cited by 50 · see citing works
“For lattice based applications, there is a richer class of parametric intrinsic autoregression models (Besag and Kooperberg 1995; Besag and Higdon 1999).”
BackgroundNonstationary Spatial Gaussian Markov Random Fields
Yu, Y., & Speckman, P. L. (2010) · Journal of Computational and Graphical Statistics · doi:10.1198/jcgs.2009.08124 · Cited by 51 · see citing works
“…assume the γ jk are smooth as well, and the prior on γ = Vec ( [ γ jk ] ) = (γ 21 ,γ 31 ,... ,γ n 1 n 2 ) is a first-order intrinsic GMRF on a regular lattice (Besag and Higdon 1999; Rue and Held 2005) subject to the constraint (2.15) Note that prior ( 2.15 ) simply extends the difference…”
BackgroundInfluentialVariational Bayes for estimating the parameters of a hidden Potts model
McGrory, C. A., Titterington, D. M., Reeves, R., et al. (2008) · Statistics and Computing · doi:10.1007/s11222-008-9095-6 · Cited by 50 · see citing works
“Recent application areas include micro-array data analysis (Gottardo et al. 2006), brain imaging (Smith and Fahrmeir 2006), disease mapping (Green and Richardson 2002) and agricultural field experiments (Besag and Higdon 1999).”
BackgroundModelling and analysing correlated non-normal data
Lee, Y., & Nelder, J. A. (2001) · Statistical Modelling · doi:10.1177/1471082x0100100102 · Cited by 49 · see citing works
A Bayesian Approach for Assessing the Stability of Genotypes
Torres, J. M. C., Crossa, J., Sanches, A., et al. (2006) · Crop Science · doi:10.2135/cropsci2006.04.0227 · Cited by 42 · see citing works
“As pointed out by Besag and Higdon (1999) , from the”
BackgroundA two-stage approach for the spatio-temporal analysis of high-throughput phenotyping data
Pérez-Valencia, D. M., Rodríguez‐Álvarez, M. X., Boer, M. P., Kronenberg, L., Hund, A., Cabrera‐Bosquet, L., Millet, E. J., & van Eeuwijk, F. A. (2022) · Scientific Reports · doi:10.1038/s41598-022-06935-9 · Cited by 37 · see citing works
Flexible modelling of spatial variation in agricultural field trials with the R package INLA
Selle, M. L., Steinsland, I., Hickey, J. M., & Gorjanc, G. (2019) · Theoretical and Applied Genetics · doi:10.1007/s00122-019-03424-y · Cited by 40 · see citing works
Markov Chain Monte Carlo With Mixtures of Mutually Singular Distributions
Gottardo, R., & Raftery, A. E. (2008) · Journal of Computational and Graphical Statistics · doi:10.1198/106186008x386102 · Cited by 36 · see citing works
“Finally, the prior for the degrees of freedom ν is uniform on the set { 1 , 2 , 4 , 8 , 16 , 32 as suggested by Besag and Higdon (1999).”
BackgroundComputation of the Information Matrix for Models With Spatial Interaction on a Lattice
Smirnov, O. A. (2005) · Journal of Computational and Graphical Statistics · doi:10.1198/106186005x78585 · Cited by 39 · see citing works
“Gaussian Markov random fields (GMRF) and spatial autoregressive models (SAR) are very popular in spatial statistics for modeling spatial processes on a lattice; see, for example, Haining (1990), Cressie (1993), Besag and Higdon (1999), among others.”
BackgroundEstimation and Prediction in the Presence of Spatial Confounding for Spatial Linear Models
Page, G. L., Liu, Y., He, Z., & Sun, D. (2017) · Scandinavian Journal of Statistics · doi:10.1111/sjos.12275 · Cited by 39 · see citing works
“Also, there is a literature that considers the bias of regression coefficients in a spatial setting when random effects are considered as formal devices to implement some type of smoothing (Green 1985, Speckman 1988, and Besag and Higdon 1999). Hodges and Reich (2010) and Paciorek (2010) also address this perspective…”
BackgroundReich, B. J., Hodges, J. S., & Carlin, B. P. (2007) · Journal of the American Statistical Association · doi:10.1198/016214506000000753 · Cited by 39 · see citing works
“…and loggamma(.01, .01) priors for the zlp , uniform(0, 10) priors on the standard deviations σlp = τ −1/2 lp , gamma(.01, .01) priors on the variances σ 2lp , and a prior motivated by Besag and Higdon (1999) with τ0p ∼ G(.01, .01), λp = τ1p + τ2p ∼ G(.01, .01), and βp = τ1p/(τ1p + τ2p) ∼ U(0, 1).”
BackgroundInfluentialGaussian Markov random field spatial models in GAMLSS
Bastiani, F. D., Rigby, R. A., Stasinopoulous, D. M., et al. (2016) · Journal of Applied Statistics · doi:10.1080/02664763.2016.1269728 · Cited by 35 · see citing works
Approximating Hidden Gaussian Markov Random Fields
Rue, H., Steinsland, I., & Erland, S. (2004) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2004.b5590.x · Cited by 35 · see citing works
“…1997), semiparametric regression (Fahrmeir and Lang, 2001a, b) and spatial and spatiotemporal models (Besag et al., 1991; Heikkinen and Arjas, 1998; Knorr-Held and Besag, 1998; Wikle et al., 1998; Besag and Higdon, 1999; Knorr-Held, 2000; Fernández and Green, 2002; Rue and Tjelmeland, 2002).”
BackgroundMethodThe analysis of quantitative traits in wheat mapping populations
Eckermann, P., Verbyla, A. P., Cullis, B. R., & Thompson, R. (2001) · Australian Journal of Agricultural Research · doi:10.1071/ar01039 · Cited by 35 · see citing works
Likelihood Inference for Models with Unobservables: Another View
Lee, Y., & Nelder, J. A. (2009) · Statistical Science · doi:10.1214/09-sts277 · Cited by 34 · see citing works
Li, Y., Wang, N., Hong, M.-Y., et al. (2007) · OakTrust (Texas A&M University Libraries) · Cited by 30 · see citing works
Evidence for conformal invariance of crop yields
McCullagh, P. J., & Clifford, D. J. (2006) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.2006.1667 · Cited by 28 · see citing works
“Bartlett (1978) showed that the Papadakis algorithm could be interpreted in terms of an explicit lattice Markov model on the plots, and this approach has been subsequently used and extended by Wilkinson et al. (1983), Besag & Kempton (1986) and Besag & Higdon (1999).”
BackgroundMethodInfluentialProbabilistic segmentation and intensity estimation for microarray images
Gottardo, R., Besag, J. E., Stephens, M., et al. (2005) · Biostatistics · doi:10.1093/biostatistics/kxi042 · Cited by 30 · see citing works
“We use a Bayesian linear model (Lindley and Smith 1972) with hierarchical-t sampling errors to allow for outliers (Besag and Higdon 1999).”
MethodSelf-citationTwo‐dimensional P‐spline smoothing for spatial analysis of plant breeding trials
Piepho, H., Boer, M. P., & Williams, E. R. (2022) · Biometrical Journal · doi:10.1002/bimj.202100212 · Cited by 29 · see citing works
“…LV⊗LVmodel of Piepho andWilliams (2010) and also to the NNmodel based on first differences along rows and columns by Kempton et al. (1994) (also see Appendix A.3; Besag & Higdon, 1999; Boer et al., 2020), and all of thesemodels can be viewed as limiting cases of the separable AR1⊗AR1model…”
BackgroundInfluentialEfficient Bayesian analysis of occupancy models with logit link functions
Clark, A. E., & Altwegg, R. (2019) · Ecology and Evolution · doi:10.1002/ece3.4850 · Cited by 26 · see citing works
“The formulation was first developed by Besag, York, and Mollié (1991) and has been extensively used in areas such as agri‐ culture (Besag & Higdon, 1999), biostatistics (Gelfand & Vounatsou, 2003; Waller & Gotway, 2004), ecology (Lichstein, Simons, Shriner, & Franzreb, 2002) and species…”
MethodAnh-Likelihood Method for Spatial Mixed Linear Models Based on Intrinsic Auto-Regressions
Dutta, S., & Mondal, D. (2014) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12084 · Cited by 25 · see citing works
Multivariate spatiotemporal modeling of age-specific stroke mortality
Quick, H., Waller, L., & Casper, M. (2016) · The Annals of Applied Statistics · doi:10.1214/17-aoas1068 · Cited by 23 · see citing works
“(1991), CAR models have sparked a wealth of research in the disease mapping context for both spatial (e.g., Besag et al., 1995; Besag and Higdon, 1999) and spatiotemporal applications.”
BackgroundBayesian Disease Mapping for Public Health
Lawson, A. B., & Lee, D. (2017) · Handbook of statistics · doi:10.1016/bs.host.2017.05.001 · Cited by 26 · see citing works
An ensemble data assimilation approach to improve farm-scale actual evapotranspiration estimation
Deb, P., Abbaszadeh, P., & Moradkhani, H. (2022) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2022.108982 · Cited by 25 · see citing works
Simple State-Space Models in a Mixed Model Framework
Piepho, H., & Ogutu, J. O. (2007) · The American Statistician · doi:10.1198/000313007x220426 · Cited by 24 · see citing works
“State-space models such as random walks have also been proposed to model trend in designed field experiments (Besag and Kempton 1986; Besag and Higdon 1999), in particular when there is a large number of treatments.”
BackgroundRivière, P., Dawson, J. C., Goldringer, I., et al. (2015) · Crop Science · doi:10.2135/cropsci2014.07.0497 · Cited by 24 · see citing works
Li, Y., Wang, N., Hong, M., et al. (2007) · The Annals of Statistics · doi:10.1214/009053607000000082 · Cited by 23 · see citing works
Bayesian estimation ofg-and-k distributions using MCMC
Haynes, M. A., & Mengersen, K. L. (2005) · Computational Statistics · doi:10.1007/bf02736120 · Cited by 23 · see citing works
“This includes the estimation of parameters in complex modelling procedures, often involving non-Gaussian distributions, such as Bayesian hierarchical modelling in applications of metaanalysis, mixture modelling (Diebolt & Robert 1994, Mengersen & Robert 1996, Gilks et al. 1996, Richardson &: Green 1997), and ranking a…”
MethodExploring Dependence with Data on Spatial Lattices
Kaiser, M. S., & Caragea, P. C. (2008) · Biometrics · doi:10.1111/j.1541-0420.2008.01118.x · Cited by 20 · see citing works
“An example of the strategy of using statistical dependencies to account for all spatial patterns exhibited by data is contained in the analysis of six agricultural field trials on corn varieties contained in Besag and Higdon (1999).”
BackgroundMethodPerformance of empirical BLUP and Bayesian prediction in small randomized complete block experiments
Forkman, J., & Piepho, H. (2012) · The Journal of Agricultural Science · doi:10.1017/s0021859612000445 · Cited by 18 · see citing works
GOODNESS OF FIT TESTS FOR A CLASS OF MARKOV RANDOM FIELD MODELS
Kaiser, M. S., Lahiri, S. N., & Nordman, D. J. (2012) · OakTrust (Texas A&M University Libraries) · Cited by 19 · see citing works
Kneib, T., Klein, N., Lang, S., & Umlauf, N. (2019) · Test · doi:10.1007/s11749-019-00631-z · Cited by 17 · see citing works
A Comparison Study on Criteria to Select the Most Adequate Weighting Matrix
Gómez, M. H., Mur, J., & Marín, M. R. (2019) · Entropy · doi:10.3390/e21020160 · Cited by 18 · see citing works
“excellent reviews in Hepple [363738], Besag and Higdon [39] and especially, LeSage and Pace [40].”
BackgroundAdjusting for Spatial Effects in Genomic Prediction
Mao, X., Dutta, S., Wong, R. K. W., & Nettleton, D. S. (2020) · Journal of Agricultural Biological and Environmental Statistics · doi:10.1007/s13253-020-00396-1 · Cited by 17 · see citing works
Goodness of fit tests for a class of Markov random field models
Kaiser, M. S., Lahiri, S. N., & Nordman, D. J. (2012) · The Annals of Statistics · doi:10.1214/11-aos948 · Cited by 23 · see citing works
“Besag and Higdon [5] present an analysis of six agricultural field trials of corn varieties conducted in North Carolina using a hierarchical model that included an intrinsic Gaussian MRF as an improper prior for spatial structure.”
MethodGelman, A., & Hill, J. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511790942.028 · Cited by 29 · see citing works
A genetic and spatial Bayesian analysis of mastitis resistance
Sæbø, S., & Frigessi, A. (2004) · Genetics Selection Evolution · doi:10.1186/1297-9686-36-5-527 · Cited by 16 · see citing works
“One advantage of the MCMC based Bayesian approach is that these probabilities, which cannot be expressed analytically, can easily be derived from the MCMC-iterates of s [2].”
MethodOkunlola, O. A., Oyeyemi, O. T., & Lukman, A. F. (2021) · Epidemiology and Health · doi:10.4178/epih.e2021041 · Cited by 15 · see citing works
“[10] owing to its ability to build models in which the contiguous spatial units inherent in the data are not lost [11-14], while also accounting for the hierarchical characteristics of the data.”
BackgroundGeostatistical Models in Agricultural Field Experiments: Investigations Based on Uniformity Trials
Richter, C., & Kroschewski, B. (2012) · Agronomy Journal · doi:10.2134/agronj2011.0100 · Cited by 15 · see citing works
Toxicity Profiling of Engineered Nanomaterials via Multivariate Dose Response Surface ModelingPreprint
Patel, T., Telesca, D., George, S., & Nel, A. E. (2011) · Collection of Biostatistics Research Archive · Cited by 14 · see citing works
The Analysis of Spatial Experiments
Pringle, M. J., Bishop, T. F. A., Lark, R. M., et al. (2010) · doi:10.1007/978-90-481-9133-8_10 · Cited by 15 · see citing works
“Besag and Higdon (1999) described the principles of the Bayesian analysis of agricultural plot trials; the same principles are applicable to large-scale, on-farm experiments, although some modifications may be needed to cope with the greater volume of data.”
BackgroundDispersion frailty models and HGLMs
Noh, M., Ha, I. D., & Lee, Y. (2005) · Statistics in Medicine · doi:10.1002/sim.2284 · Cited by 15 · see citing works
Hansen, M. B. (2002) · Biostatistics · doi:10.1093/biostatistics/3.2.213 · Cited by 15 · see citing works
“In this case note that in many Bayesian applications (including but not restricted to deformable template modelling) an improper prior density is used instead, provided the posterior becomes proper: see for example, Besag and Kooperberg (1995), Besag and Higdon (1999), and Kent et al. (1996). When κ is fixed, the impr…”
BackgroundMethodBoer, M. P., Piepho, H., & Williams, E. R. (2020) · Journal of Agricultural Biological and Environmental Statistics · doi:10.1007/s13253-020-00412-4 · Cited by 13 · see citing works
Weighted Estimation of AMMI and GGE Models
Hadasch, S., Forkman, J., Malik, W. A., & Piepho, H. (2018) · Journal of Agricultural Biological and Environmental Statistics · doi:10.1007/s13253-018-0323-z · Cited by 13 · see citing works
“The dataset used as a basis for the simulation is a MET comprising observations of yield from64 corn hybrids tested in six environmentswith three replicates per environment (Besag and Higdon 1999).”
MethodToxicity profiling of engineered nanomaterials via multivariate dose-response surface modeling
Patel, T., Telesca, D., George, S., & Nel, A. E. (2012) · The Annals of Applied Statistics · doi:10.1214/12-aoas563 · Cited by 14 · see citing works
“We specified the prior to be uniform on 1, 2, 4, 8, 16 and 32 degrees of freedom [Besag and Higdon (1999)].”
InfluentialVoxel-Based Adaptive Spatio-Temporal Modelling of Perfusion Cardiovascular MRI
Schmid, V. J. (2011) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2011.2109733 · Cited by 14 · see citing works
“This can be done via the Kroneckermatrix sum of both precision matrices [22].”
MethodFast sampling from a Gaussian Markov random field using Krylov subspace approaches
Simpson, D., Turner, I., & Pettitt, A. N. (2008) · QUT ePrints (Queensland University of Technology) · Cited by 14 · see citing works
Noh, M., Lee, Y., Oh, S., et al. (2012) · Osong Public Health and Research Perspectives · doi:10.1016/j.phrp.2012.11.003 · Cited by 13 · see citing works
“Spatial Model 2 is the intrinsic autoregressive model of Besag and Higdon [7], uiwIAR with kernelP iwjðui ujÞ2=l1.”
MethodPrediction interval for disease mapping using hierarchical likelihood
Lee, Y., Jang, M.-J., & Lee, W. (2011) · Computational statistics (Zeitschrift) · doi:10.1007/s00180-010-0215-3 · Cited by 12 · see citing works
Juan, P., Díaz‐Ávalos, C., Mejía‐Domínguez, N. R., et al. (2016) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-016-1340-5 · Cited by 12 · see citing works
“On the other hand, MCMC uses the neighbor structure imposed by the modeler, resulting in noisier maps, depending of whether a first or higher order neighbor structure is used (Besag and Higdon 1999).”
MethodRelating nano‐particle properties to biological outcomes in exposure escalation experiments
Patel, T., Telesca, D., Low‐Kam, C., Ji, Z., Zhang, H. Y., Xia, T., Zinc, J. I., & Nel, A. E. (2013) · Environmetrics · doi:10.1002/env.2246 · Cited by 12 · see citing works
“Specifically, the prior was modeled using a discrete uniform distribution on 1, 2, 4, 8, 16 and 32 degrees of freedom (Besag and Higdon, 1999).”
Re-considering the variance parameterization in multiple precision models
Carlin, B. P., He, Y. L., & Hodges, J. S. (2007) · Bayesian Analysis · doi:10.1214/07-ba221 · Cited by 12 · see citing works
“The simplex parameterization (Besag & Higdon 1999), which we now introduce, appears to avoid or mitigate these difficulties.”
BackgroundMethodInfluentialQuality Control and Robust Estimation for cDNA Microarrays With Replicates
Gottardo, R., Raftery, A. E., Yeung, K. Y., et al. (2006) · Journal of the American Statistical Association · doi:10.1198/016214505000001096 · Cited by 12 · see citing works
“The robustness is achieved using a hierarchical-t formulation (Besag and Higdon 1999), which is more robust than the usual Gaussian model.”
BackgroundMethodInfluentialvan Frank, G., Goldringer, I., Rivière, P., et al. (2019) · Euphytica · doi:10.1007/s10681-019-2447-9 · Cited by 10 · see citing works
A Bayesian analysis of an agricultural field trial with three spatial dimensions
Donald, M. R., Alston‐Knox, C., Young, R. R., & Mengersen, K. L. (2011) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2011.06.022 · Cited by 10 · see citing works
Hess, D. E., van Lieshout, M.-C., Payne, B., et al. (2001) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/s0303-2434(01)85004-2 · Cited by 10 · see citing works
“…black is used for high values. although agricultural applications have played a major role in the statistical literature from its beginning [eg, Fisher, 1928], most studies took a frequentist point of view, despite the obvious spatial correlation present in fields [Besag & Higdon, 1993; 1999].”
BackgroundA brief review of the classic methods of experimental statistics
de Carvalho, A. M. X., Mendes, F. Q., Borges, P. H. D. C., & Kramer, M. (2022) · Acta Scientiarum Agronomy · doi:10.4025/actasciagron.v45i1.56882 · Cited by 8 · see citing works
REML estimation with intrinsic Matérn dependence in the spatial linear mixed model
Dutta, S., & Mondal, D. (2016) · Electronic Journal of Statistics · doi:10.1214/16-ejs1125 · Cited by 9 · see citing works
“The idea was put forward by Besag and Higdon (1999), purely as a computational ploy, to alleviate edge effects, and, in practice, its implementation requires just a straightforward adjustment to F .”
Point estimates for variance-structure parameters in Bayesian analysis of hierarchical models
He, Y. L., & Hodges, J. S. (2007) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2007.08.021 · Cited by 9 · see citing works
Une nouvelle construction de champs Gaussiens à partir de chaînes de Markov
Diaconis, P. W. (2002) · Annales de l Institut Henri Poincaré Probabilités et Statistiques · doi:10.1016/s0246-0203(02)01123-8 · Cited by 9 · see citing works
A Review of Spatiotemporal Models for Count Data in R Packages. A Case Study of COVID-19 Data
Ibáñez, M. V., Martínez-Garcia, M., & Simó, A. (2021) · Mathematics · doi:10.3390/math9131538 · Cited by 8 · see citing works
“Historically, spatial and temporal models for count data are needed in a variety of settings: agriculture production ([Besag and Higdon(1999)]), fishing captures ([Paradinas et al.(2017)Paradinas, Conesa, L´opez-Qu´ılez, and Bellido]), volcano eruptions ([Gusev(2008)]), crime counts ([Law et…”
BackgroundKapuku, C., Cho, S., Kho, S., et al. (2019) · IET Intelligent Transport Systems · doi:10.1049/iet-its.2019.0159 · Cited by 8 · see citing works
“The most popular that has been applied recently in transportation is the intrinsic autoregressive model [20].”
BackgroundMethodInfluentialModelling Replicated Weed Growth Data using Spatially-varying Growth Curves
Banerjee, S., Johnson, G. A., Schneider, N., et al. (2005) · Environmental and Ecological Statistics · doi:10.1007/s10651-005-1519-2 · Cited by 8 · see citing works
“In the spatial statistics literature, Besag and Higdon (1999) have investigated statistical modelling of agricultural field experiments.”
BackgroundMethodBayesian P-Splines in Structured Additive Regression Models
Brezger, A. (2005) · doi:10.5282/edoc.3942 · Cited by 8 · see citing works
- Authors
- Besag, J., & Higdon, D.
- Affiliations
- University of Washington (US)
- Duke University (US)
- Corresponding author
- Besag, Julian
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 1999-11-01
- Volume
- 61
- Issue
- 4
- Pages
- 691-746
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W1990075873
- Cited by
- 183
- References
- 88
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 6 · 2023: 2 · 2022: 7 · 2021: 10 · 2020: 4 · 2019: 8 · 2018: 9 · 2017: 8 · 2016: 5 · 2015: 2 · 2014: 5 · 2013: 3 · 2012: 9
- FWCI
- 9.79
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Journal of the Royal Statistical Society Series B (Statistical Methodology) (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Kent Academic Repository (University of Kent) (submittedVersion)
- Funders
- Durham University
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Genetics and Plant Breeding
- Keywords
- Bayesian analysis, agricultural field experiments, spatial modeling, outliers, cultivar evaluation, crop yield, binary data, ranking and selection, Markov chain Monte Carlo
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-02.
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