Cited by 570Statistical Methods and Bayesian InferenceOptimal Experimental Design MethodsAgriculture, Soil, Plant Science

The DOI matches its Crossref record; 78 of the 78 references it cites were found in a registry or an index.

The Analysis of Designed Experiments and Longitudinal Data by Using Smoothing Splines

Verbyla, Arũnas P., Cullis, Brian R., Kenward, Michael G., Welham, Sue J.·1999·Journal of the Royal Statistical Society Series C: Applied Statistics

DOI 10.1111/1467-9876.00154

Open this paper in CoChat — free account

Abstract

SUMMARY: In designed experiments and in particular longitudinal studies, the aim may be to assess the effect of a quantitative variable such as time on treatment effects. Modelling treatment effects can be complex in the presence of other sources of variation. Three examples are presented to illustrate an approach to analysis in such cases. The first example is a longitudinal experiment on the growth of cows under a factorial treatment structure where serial correlation and variance heterogeneity complicate the analysis. The second example involves the calibration of optical density and the concentration of a protein DNase in the presence of sampling variation and variance heterogeneity. The final example is a multienvironment agricultural field experiment in which a yield–seeding rate relationship is required for several varieties of lupins. Spatial variation within environments, heterogeneity between environments and variation between varieties all need to be incorporated in the analysis. In this paper, the cubic smoothing spline is used in conjunction with fixed and random effects, random coefficients and variance modelling to provide simultaneous modelling of trends and covariance structure. The key result that allows coherent and flexible empirical model building in complex situations is the linear mixed model representation of the cubic smoothing spline. An extension is proposed in which trend is partitioned into smooth and non-smooth components. Estimation and inference, the analysis of the three examples and a discussion of extensions and unresolved issues are also presented.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1111/1467-9876.00154

Cites

78 of 78 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Random-Effects Models for Longitudinal Data
Laird, N. M., & Ware, J. H. (1982) · Biometrics · doi:10.2307/2529876 · Cited by 8,923
Generalized Additive Models.
Brown, R. A., Hastie, T. J., & Tibshirani, R. J. (1991) · Biometrics · doi:10.2307/2532174 · Cited by 8,263
Analysis of Longitudinal Data
Everitt, B. S. (2001) · Psychology Press eBooks · doi:10.4324/9781410605542-11 · Cited by 6,877
Approximate Inference in Generalized Linear Mixed Models
Breslow, N. E., & Clayton, D. G. (1993) · Journal of the American Statistical Association · doi:10.1080/01621459.1993.10594284 · Cited by 4,662
Small sample inference for fixed effects from restricted maximum likelihood
Kenward, M. G., & Roger, J. H. (1997) · Biometrics · doi:10.2307/2533558 · Cited by 4,552
Self-citation
Flexible smoothing with B-splines and penalties
Eilers, P., & Marx, B. (1996) · Statistical Science · doi:10.1214/ss/1038425655 · Cited by 4,159
Recovery of Inter-Block Information when Block Sizes are Unequal
Patterson, H. D., & Thompson, R. (1971) · Biometrika · doi:10.1093/biomet/58.3.545 · Cited by 4,073
Asymptotic Properties of Maximum Likelihood Estimators and Likelihood Ratio Tests under Nonstandard Conditions
Self, S., & Liang, K.-Y. (1987) · Journal of the American Statistical Association · doi:10.1080/01621459.1987.10478472 · Cited by 2,861
Nonparametric Regression and Generalized Linear Models
Green, P. J., & Silverman, B. W. (1994) · Springer eBooks · doi:10.1007/978-1-4899-4473-3 · Cited by 1,935
Approximate Inference in Generalized Linear Mixed Models
(1993) · doi:10.2307/2290687 · Cited by 1,800

Show all 73 references

Research works printed but not linked: 6

[19]19. Gilmour, A. R., Thompson, R., Cullis, B. R. and Welham, S. J. (1996) ASREML. Biometric Bulletin. New South Wales Agriculture, Wagga Wagga.
[28]28. Kenward, M. G. and Welham, S. J. (1995) Use of splines in random coecient regression models for the analysis of repeated measurements. In Proc. Symp. Statistical Software 95, pp. 95±112. Utrecht: University of Utrecht.
[42]42. Searle, S. R., Casella, G. and McCulloch, C. E. (1992) Variance Components. New York: Wiley.
[60]60. Box, G. E. P. and Draper, N. R. (1987) Empirical Modelling and Response Surfaces. New York: Wiley.
[65]65. Diggle, P. J., Liang, K.-Y. and Zeger, S. L. (1994) Analysis of Longitudinal Data. Oxford: Clarendon.
[66]66. Durban, M. (1999) Modelling spatial trends and local competition eects using semiparametric additive models. PhD Thesis. Heriot-Watt University, Edinburgh.

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
Hierarchical generalized additive models in ecology: an introduction with mgcv
Pedersen, E. J., Miller, D. L., Simpson, G. L., & Ross, N. (2019) · PeerJ · doi:10.7717/peerj.6876 · Cited by 1,225
Inference in Generalized Additive Mixed Models by Using Smoothing Splines
Lin, X., & Zhang, D. (1999) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/1467-9868.00183 · Cited by 653
Low‐Rank Scale‐Invariant Tensor Product Smooths for Generalized Additive Mixed Models
Wood, S. N. (2006) · Biometrics · doi:10.1111/j.1541-0420.2006.00574.x · Cited by 613
ASREML user guide release 1.0
Gilmour, A. R., Gogel, B. J., Cullis, B. R., Welham, S. J., & Thompson, R. (2002) · Rothamsted Repository (Rothamsted Repository) · Cited by 564
Self-citation
A Hitchhiker's Guide to Mixed Models for Randomized Experiments
Piepho, H., Büchse, A., & Emrich, K. (2003) · Journal of Agronomy and Crop Science · doi:10.1046/j.1439-037x.2003.00049.x · Cited by 364
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
Nonparametric Function Estimation for Clustered Data When the Predictor is Measured without/with Error
Lin, X., & Carroll, R. J. (2000) · Journal of the American Statistical Association · doi:10.1080/01621459.2000.10474229 · Cited by 318
Smoothing and mixed models
WAND, M. P. (2003) · Computational Statistics · doi:10.1007/s001800300142 · Cited by 295
Child Mortality Estimation 2013: An Overview of Updates in Estimation Methods by the United Nations Inter-Agency Group for Child Mortality Estimation
Alkema, L., New, J. R., Pedersen, J., You, D., & all members of the UN Inter-agency Group for Child Mortality Estimation and its Technical Advisory Group (2014) · PLoS ONE · doi:10.1371/journal.pone.0101112 · Cited by 290
Splines, knots, and penalties
Eilers, P. H. C., & Marx, B. D. (2010) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.125 · Cited by 288
Functional Mixed Effects Models
Guo, W. (2002) · Biometrics · doi:10.1111/j.0006-341x.2002.00121.x · Cited by 256
Semiparametric regression during 2003–2007
Ruppert, D., WAND, M. P., & Carroll, R. J. (2009) · Electronic Journal of Statistics · doi:10.1214/09-ejs525 · Cited by 248
Nitrogen and phosphorus removal from plant nursery runoff in vegetated and unvegetated subsurface flow wetlands
Huett, D. O., Morris, S., Smith, G., & Hunt, N. (2005) · Water Research · doi:10.1016/j.watres.2005.05.038 · Cited by 243
A methodological framework for model selection in interrupted time series studies
Bernal, J. L., Soumerai, S. B., & Gasparrini, A. (2018) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2018.05.026 · Cited by 255
Biochar lowers ammonia emission and improves nitrogen retention in poultry litter composting
Agyarko-Mintah, E., Cowie, A., ZWIETEN, L. V., Singh, B. P., Smillie, R., Harden, S., & Fornasier, F. (2016) · Waste Management · doi:10.1016/j.wasman.2016.12.009 · Cited by 235
Joint modeling of additive and non-additive genetic line effects in single field trials
Oakey, H., Verbyla, A. P., Pitchford, W. S., Cullis, B. R., & Kuchel, H. (2006) · Theoretical and Applied Genetics · doi:10.1007/s00122-006-0333-z · Cited by 213
Self-citation
Fast and compact smoothing on large multidimensional grids
Eilers, P. H. C., Currie, I. D., & Durbán, M. (2004) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2004.07.008 · Cited by 206
Generalized Linear Array Models with Applications to Multidimensional Smoothing
Currie, I. D., Durbán, M., & Eilers, P. H. C. (2006) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2006.00543.x · Cited by 205
Random regression analyses using B-splines to model growth of Australian Angus cattle
Meyer, K. (2005) · Genetics Selection Evolution · doi:10.1186/1297-9686-37-6-473 · Cited by 204
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
Smoothing Parameter Selection for a Class of Semiparametric Linear Models
Reiss, P. T., & Ogden, R. T. (2009) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2008.00695.x · Cited by 201
Straightforward intermediate rank tensor product smoothing in mixed models
Wood, S. N., Scheipl, F., & Faraway, J. J. (2012) · Statistics and Computing · doi:10.1007/s11222-012-9314-z · Cited by 198
The spatio‐temporal distribution of Mycobacterium bovis (bovine tuberculosis) infection in a high‐density badger population
Delahay, R. J., Langton, S., Smith, G. C., Clifton‐Hadley, R. S., & Cheeseman, C. L. (2000) · Journal of Animal Ecology · doi:10.1046/j.1365-2656.2000.00406.x · Cited by 182
Spatial analysis methods for forest genetic trials
Dutkowski, G., Silva, J. C. E., Gilmour, A. R., & Lopez, G. A. (2002) · Canadian Journal of Forest Research · doi:10.1139/x02-111 · Cited by 179
Comparing Curves Using Additive Models
Walker, E., & Wright, S. P. (2002) · Journal of Quality Technology · doi:10.1080/00224065.2002.11980134 · Cited by 175
Simple fitting of subject‐specific curves for longitudinal data
Durbán, M., Harezlak, J., WAND, M. P., & Carroll, R. J. (2004) · Statistics in Medicine · doi:10.1002/sim.1991 · Cited by 170
Visualization and Curve-Parameter Estimation Strategies for Efficient Exploration of Phenotype Microarray Kinetics
Vaas, L. A. I., Sikorski, J., Michael, V., Göker, M., & Klenk, H. (2012) · PLoS ONE · doi:10.1371/journal.pone.0034846 · Cited by 165
A Comparison of Arthropod Communities in Transgenic Bt and Conventional Cotton in Australia
Whitehouse, M., Wilson, L., & Fitt, G. P. (2005) · Environmental Entomology · doi:10.1603/0046-225x(2005)034[1224:acoaci]2.0.co;2 · Cited by 161
Parametric modelling of growth curve data: An overview
Zimmerman, D. L., Núñez‐Antón, V., Grégoire, T. G., Schabenberger, O., Hart, J. D., Kenward, M. G., Molenberghs, G., Verbeke, G. N., Pourahmadi, M., Vieu, P., et al. (2001) · Test · doi:10.1007/bf02595823 · Cited by 160
Self-citation
Global patterns of change and variation in sea surface temperature and chlorophyll a
Dunstan, P. K., Foster, S. D., King, E. A., Risbey, J. S., Okane, T., Monselesan, D. P., Hobday, A. J., Hartog, J. R., & THOMPSON, P. A. (2018) · Scientific Reports · doi:10.1038/s41598-018-33057-y · Cited by 168
Soil carbon dynamics under different cropping and pasture management in temperate Australia: Results of three long-term experiments
Chan, K. Y., Conyers, M. K., Li, G. D., Helyar, K. R., Poile, G. J., Oates, A. A., & Barchia, I. M. (2011) · Soil Research · doi:10.1071/sr10185 · Cited by 152
Random regressions to model phenotypic variation in monthly weights of Australian beef cows
Meyer, K. (2000) · Livestock Production Science · doi:10.1016/s0301-6226(99)00183-9 · Cited by 138
Yield-related salinity tolerance traits identified in a nested association mapping (NAM) population of wild barley
Saadé, S., Maurer, A., Shahid, M., Oakey, H., Schmöckel, S. M., Negrão, S., Pillen, K., & Tester, M. A. (2016) · Scientific Reports · doi:10.1038/srep32586 · Cited by 137
Genetic modelling of daily milk yield using orthogonal polynomials and parametric curves
Brotherstone, S., White, I. M. S., & Meyer, K. (2000) · Animal Science · doi:10.1017/s1357729800051754 · Cited by 136
The Accuracy of Seven Mathematical Functions in Modeling Dairy Cattle Lactation Curves Based on Test-Day Records From Varying Sample Schemes
Silvestre, A. M., Petim-Batista, F., & Colaço, J. (2006) · Journal of Dairy Science · doi:10.3168/jds.s0022-0302(06)72250-0 · Cited by 136
Flexible smoothing with P-splines: a unified approach
Currie, I. D., & Durbán, M. (2002) · Statistical Modelling · doi:10.1191/1471082x02st039ob · Cited by 135
A review on linear mixed models for longitudinal data, possibly subject to dropout
Molenberghs, G., & Verbeke, G. N. (2001) · Statistical Modelling · doi:10.1177/1471082x0100100402 · Cited by 125
Analysis of early tree height in forest genetic trials is enhanced by including a spatially correlated residual
Silva, J. C. E., Dutkowski, G., & Gilmour, A. R. (2001) · Canadian Journal of Forest Research · doi:10.1139/x01-123 · Cited by 125
Genotype × Environment Interaction for Milk Production of Daughters of Australian Dairy Sires from Test-Day Records
Hayes, B. J., Carrick, M., Bowman, P. J., & Goddard, M. E. (2003) · Journal of Dairy Science · doi:10.3168/jds.s0022-0302(03)73980-0 · Cited by 123
Impact of nitrification inhibitor (DMPP) on soil nitrous oxide emissions from an intensive broccoli production system in sub-tropical Australia
Scheer, C., Rowlings, D. W., Firrell, M., Deuter, P., Morris, S., & Grace, P. (2014) · Soil Biology and Biochemistry · doi:10.1016/j.soilbio.2014.07.006 · Cited by 122
Bread Wheat With High Salinity and Sodicity Tolerance
Genc, Y., Taylor, J., Lyons, G. H., Li, Y., Cheong, J., Appelbee, M. J., Oldach, K., & Sutton, T. (2019) · Frontiers in Plant Science · doi:10.3389/fpls.2019.01280 · Cited by 120
The mathematical description of lactation curves in dairy cattle
Macciotta, N. P. P., Dimauro, C., Rassu, S. P. G., Steri, R., & Pulina, G. (2011) · Italian Journal of Animal Science · doi:10.4081/ijas.2011.e51 · Cited by 115
Modelling spatial trends in sorghum breeding field trials using a two-dimensional P-spline mixed model
Velazco, J. G., Rodríguez‐Álvarez, M. X., Boer, M. P., Jordan, D. R., Eilers, P. H. C., Malosetti, M., & van Eeuwijk, F. A. (2017) · Theoretical and Applied Genetics · doi:10.1007/s00122-017-2894-4 · Cited by 119
Genetic parameters for various random regression models to describe the weight data of pigs
Huisman, A., Veerkamp, R. F., & van Arendonk, J. A. M. (2002) · Journal of Animal Science · doi:10.2527/2002.803575x · Cited by 110
Experimental Agriculture
(1951) · Nature · doi:10.1038/168232b0 · Cited by 109
Effects of precalving body condition score and prepartum feeding level on production, reproduction, and health parameters in pasture-based transition dairy cows
Roche, J. R., Meier, S., Heiser, A., Mitchell, M. D., Walker, C. G., Crookenden, M. A., Vailati-Riboni, M., Loor, J. J., & Kay, J. K. (2015) · Journal of Dairy Science · doi:10.3168/jds.2014-9269 · Cited by 108
Regional variation in wood specific gravity of planted loblolly pine in the United States
Jordan, L., Clark, A., Schimleck, L. R., Hall, D. B., & Daniels, R. F. (2008) · Canadian Journal of Forest Research · doi:10.1139/x07-158 · Cited by 106
A modified sulphur hexafluoride tracer technique enables accurate determination of enteric methane emissions from ruminants
Deighton, M. H., Williams, S. R. O., Hannah, M. C., Eckard, R., Boland, T. M., Wales, W. J., & Moate, P. J. (2014) · Animal Feed Science and Technology · doi:10.1016/j.anifeedsci.2014.08.003 · Cited by 106
Relationships between shear rheology and sensory attributes of hydrocolloid-thickened fluids designed to compensate for impairments in oral manipulation and swallowing
Ross, A. I. V., Tyler, P., Borgognone, M. G., & Eriksen, B. M. (2019) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2019.05.040 · Cited by 105
Estimation and utilisation of glutenin gene effects from the analysis of unbalanced data from wheat breeding programs
Eagles, H. A., Hollamby, G. J., Gororo, N. N., & Eastwood, R. F. (2002) · Australian Journal of Agricultural Research · doi:10.1071/ar01074 · Cited by 105
Enteral nutrition in patients with acute renal failure
Fiaccadori, E., Maggiore, U., Giacosa, R., Rotelli, C., Picetti, E., Sagripanti, S., Melfa, L., Meschi, T., Borghi, L., & Cabassi, A. (2004) · Kidney International · doi:10.1111/j.1523-1755.2004.00459.x · Cited by 103
Calving body condition score affects indicators of health in grazing dairy cows
Roche, J. R., Macdonald, K. A., Schütz, K. E., Matthews, L. R., Verkerk, G. A., Meier, S., Loor, J. J., Rogers, A. R., McGowan, J., Morgan, S. R., et al. (2013) · Journal of Dairy Science · doi:10.3168/jds.2013-6600 · Cited by 103
A genetic evaluation of growth in sheep using random regression techniques
Lewis, R. M., & Brotherstone, S. (2002) · Animal Science · doi:10.1017/s1357729800052218 · Cited by 102
Plant-biochar interactions drive the negative priming of soil organic carbon in an annual ryegrass field system
Weng, Z. H., ZWIETEN, L. V., Singh, B. P., Kimber, S. W. L., Morris, S., Cowie, A., & Macdonald, L. M. (2015) · Soil Biology and Biochemistry · doi:10.1016/j.soilbio.2015.08.005 · Cited by 105
QTL Mapping of Partial Resistance to Field Epidemics of Ascochyta Blight of Pea
Timmerman‐Vaughan, G. M., Frew, T. J., Russell, A. C., Khan, T. N., Butler, R. C., Gilpin, M. J., Murray, S., & Falloon, K. (2002) · Crop Science · doi:10.2135/cropsci2002.2100 · Cited by 100
Smoothing spline ANOVA models for large data sets with Bernoulli observations and the randomized GACV
Gao, F., Klein, R., Klein, R., Lin, X., Wahba, G., & Xiang, D. (2000) · The Annals of Statistics · doi:10.1214/aos/1015957471 · Cited by 100
Twenty years of P-splines
Eilers, P. H. C., Marx, B. D., & Durbán, M. (2015) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · doi:10.2436/20.8080.02.25 · Cited by 98
Genotype-by-management interactions for grain yield and grain protein concentration of wheat
Cooper, M., Woodruff, D. R., Phillips, I. G., Basford, K. E., & Gilmour, A. R. (2001) · Field Crops Research · doi:10.1016/s0378-4290(00)00131-3 · Cited by 98
Environmental and lunar cues are predictive of the timing of river entry and spawning‐site arrival in lake sturgeon Acipenser fulvescens
Forsythe, P. S., Scribner, K. T., Crossman, J. A., Ragavendran, A., Baker, E. A., Davis, C., & Smith, K. (2012) · Journal of Fish Biology · doi:10.1111/j.1095-8649.2012.03308.x · Cited by 97
Dissecting genetic and non-genetic sources of long-term yield trend in German official variety trials
Piepho, H., Laidig, F., Drobek, T., & Meyer, U. (2014) · Theoretical and Applied Genetics · doi:10.1007/s00122-014-2275-1 · Cited by 95
Genetic and phenotypic trends and parameters in reproduction, greasy fleece weight and liveweight in Merino lines divergently selected for multiple rearing ability
Cloete, S. W. P., Gilmour, A. R., Olivier, J. J., & van Wyk, J. B. (2004) · Australian Journal of Experimental Agriculture · doi:10.1071/ea02225 · Cited by 94
P -spline ANOVA-type interaction models for spatio-temporal smoothing
Lee, D., & Durbán, M. (2011) · Statistical Modelling · doi:10.1177/1471082x1001100104 · Cited by 91
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
Self-citation
Effects of birth weight and postnatal nutrition on neonatal sheep: III. Regulation of energy metabolism1,2
Greenwood, P. L., Hunt, A. S., Slepetis, R. M., Finnerty, K., Alston, C., Beermann, D. H., & Bell, A. W. (2002) · Journal of Animal Science · doi:10.2527/2002.80112850x · Cited by 88
Shrinkage and Penalized Likelihood as Methods to Improve Predictive Accuracy
van Houwelingen, H. C. (2001) · Statistica Neerlandica · doi:10.1111/1467-9574.00154 · Cited by 86
A COMPARISON OF MIXED MODEL SPLINES FOR CURVE FITTING
Welham, S. J., Cullis, B. R., Kenward, M. G., & Thompson, R. (2007) · Australian & New Zealand Journal of Statistics · doi:10.1111/j.1467-842x.2006.00454.x · Cited by 85
Self-citation
Estimation and inference in functional mixed-effects models
Antoniadis, A., & Sapatinas, T. (2006) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2006.09.038 · Cited by 85
Validation of quantitative trait loci for Ascochyta blight resistance in pea (Pisum sativum L.), using populations from two crosses
Timmerman‐Vaughan, G. M., Frew, T. J., Butler, R. C., Murray, S., Gilpin, M. J., Falloon, K., Johnston, P. A., Lakeman, M. B., Russell, A. C., & Khan, T. N. (2004) · Theoretical and Applied Genetics · doi:10.1007/s00122-004-1779-5 · Cited by 84
A water deficit during pod development in lentils reduces flower and pod numbers but not seed size
Shrestha, R., Turner, N. C., Siddique, K. H. M., Turner, D. W., & Speijers, J. (2006) · Australian Journal of Agricultural Research · doi:10.1071/ar05225 · Cited by 83
Genotype by Environment Interaction for Fertility, Survival, and Milk Production Traits in Australian Dairy Cattle
Haile‐Mariam, M., Carrick, M., & Goddard, M. E. (2008) · Journal of Dairy Science · doi:10.3168/jds.2008-1084 · Cited by 83
Relationship between colorimetric (instrumental) evaluation and consumer-defined beef colour acceptability
Holman, B. W. B., Mao, Y., Coombs, C. E. O., van de Ven, R. J., & Hopkins, D. L. (2016) · Meat Science · doi:10.1016/j.meatsci.2016.05.002 · Cited by 83
Simulation of biomass and sugar accumulation in sugarcane using a process-based model
Liu, D. L., & Bull, T. A. (2001) · Ecological Modelling · doi:10.1016/s0304-3800(01)00372-6 · Cited by 82
Global estimation of child mortality using a Bayesian B-spline Bias-reduction model
Alkema, L., & New, J. R. (2014) · The Annals of Applied Statistics · doi:10.1214/14-aoas768 · Cited by 82
Global estimation of child mortality using a Bayesian B-spline bias-reduction modelPreprint
Alkema, L., & New, J. R. (2014) · Cited by 82
Winter Nitrate Leaching under Different Tillage and Winter Cover Crop Management Practices
Fraser, P. M., Curtin, D., Harrison-Kirk, T., Meenken, E. D., Beare, M., Tabley, F. J., Gillespie, R. N., & Francis, G. S. (2013) · Soil Science Society of America Journal · doi:10.2136/sssaj2012.0256 · Cited by 80
Practical Smoothing
Eilers, P. H. C., & Marx, B. D. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108610247 · Cited by 79
Varietal selection for perennial crops where data relate to multiple harvests from a series of field trials
Smith, A. B., Stringer, J. K., Wei, X., & Cullis, B. R. (2007) · Euphytica · doi:10.1007/s10681-007-9418-2 · Cited by 78
Self-citation
Sustainable grazing systems for the Central Tablelands, New South Wales
Michalk, D. L., Dowling, P. M., Kemp, D. R., King, W. M., Packer, I. J., Holst, P. J., Jones, R. E., Priest, S., Millar, G. D., Brisbane, S., et al. (2003) · Australian Journal of Experimental Agriculture · doi:10.1071/ea02180 · Cited by 77
Genetic control of interactions among individuals: contrasting outcomes of indirect genetic effects arising from neighbour disease infection and competition in a forest tree
Silva, J. C. E., Potts, B. M., Bijma, P., Kerr, R. J., & Pilbeam, D. J. (2012) · New Phytologist · doi:10.1111/nph.12035 · Cited by 76
A note on model selection using information criteria for general linear models estimated using REML
Verbyla, A. P. (2019) · Australian & New Zealand Journal of Statistics · doi:10.1111/anzs.12254 · Cited by 76
Self-citation
Generalized Linear Additive Smooth Structures
Eilers, P. H. C., & Marx, B. D. (2002) · Journal of Computational and Graphical Statistics · doi:10.1198/106186002321018777 · Cited by 76
Rams with poor feed efficiency are highly responsive to an exogenous adrenocorticotropin hormone (ACTH) challenge
Knott, S., Cummins, L. J., Dunshea, F. R., & Leury, B. J. (2007) · Domestic Animal Endocrinology · doi:10.1016/j.domaniend.2007.07.002 · Cited by 75
Linkage Mapping of QTL for Seed Yield, Yield Components, and Developmental Traits in Pea
Timmerman‐Vaughan, G. M., Mills, A., Whitfield, C. P., Frew, T. J., Butler, R. C., Murray, S., Lakeman, M. B., McCallum, J. A., Russell, A. C., & Wilson, D. R. (2005) · Crop Science · doi:10.2135/cropsci2004.0436 · Cited by 75
A note on smoothing parameter selection for penalized spline smoothing
Kauermann, G. (2003) · Journal of Statistical Planning and Inference · doi:10.1016/j.jspi.2003.09.023 · Cited by 74
Insulin resistance in divergent strains of Holstein-Friesian dairy cows offered fresh pasture and increasing amounts of concentrate in early lactation.
Chagas, L., Lucy, M. C., Back, P., Blache, D., Lee, J., Gore, P., Sheahan, A., & Roche, J. (2009) · Journal of Dairy Science · doi:10.3168/jds.2008-1329 · Cited by 79
Improved management of Avena ludoviciana and Phalaris paradoxa with more densely sown wheat and less herbicide
Walker, S. R., Medd, R. W., Robinson, G., & Cullis, B. R. (2002) · Weed Research · doi:10.1046/j.1365-3180.2002.00283.x · Cited by 72
Self-citation
Milk production responses to pre- and postcalving dry matter intake in grazing dairy cows
Roche, J. R. (2006) · Livestock Science · doi:10.1016/j.livsci.2006.08.016 · Cited by 70
Nearest 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
Rapid Parallel Evolution of Azole Fungicide Resistance in Australian Populations of the Wheat Pathogen Zymoseptoria tritici
McDonald, M. C., Renkin, M., Spackman, M., Orchard, B. A., Croll, D., Solomon, P. S., & Milgate, A. (2018) · Applied and Environmental Microbiology · doi:10.1128/aem.01908-18 · Cited by 69
Modelling growth of Antarctic krill. I. Growth trends with sex, length, season, and region
Kawaguchi, S., Candy, S., King, R. A., Naganobu, M., & Nicol, S. (2006) · Marine Ecology Progress Series · doi:10.3354/meps306001 · Cited by 68
Analysing repeated measurements in soil monitoring and experimentation
Webster, R., & Payne, R. W. (2002) · European Journal of Soil Science · doi:10.1046/j.1365-2389.2002.00408.x · Cited by 67
The Impact of Electronic Health Records on Workflow and Financial Measures in Primary Care Practices
Fleming, N. S., Becker, E. R., Culler, S. D., Cheng, D., McCorkle, R., da Graca, B., & Ballard, D. J. (2013) · Health Services Research · doi:10.1111/1475-6773.12133 · Cited by 67
Effects of grazing method and fertiliser inputs on the productivity and sustainability of phalaris-based pastures in Western Victoria
Chapman, D. F., McCaskill, M. R., Quigley, P. E., Thompson, A. N., Graham, J. F., Borg, D., Lamb, J., Kearney, G. A., Saul, G. R., & Clark, S. G. (2003) · Australian Journal of Experimental Agriculture · doi:10.1071/ea02198 · Cited by 67
A probabilistic model for simultaneous exposure to multiple compounds from food and its use for risk–benefit assessment
van der Voet, H., de Mul, A., & van Klaveren, J. D. (2007) · Food and Chemical Toxicology · doi:10.1016/j.fct.2007.02.009 · Cited by 66
Uncoupling of sodium and chloride to assist breeding for salinity tolerance in crops
Genc, Y., Oldach, K., Taylor, J., & Lyons, G. H. (2015) · New Phytologist · doi:10.1111/nph.13757 · Cited by 66
Use of Growth Indices from Radiometric Culture for Quantification of Sheep Strains ofMycobacterium aviumsubsp.paratuberculosis
Reddacliff, L. A., Nicholls, P. J., Vadali, A., & Whittington, R. J. (2003) · Applied and Environmental Microbiology · doi:10.1128/aem.69.6.3510-3516.2003 · Cited by 66
Dynamic Conditionally Linear Mixed Models for Longitudinal Data
Pourahmadi, M., & Daniels, M. J. (2002) · Biometrics · doi:10.1111/j.0006-341x.2002.00225.x · Cited by 65
Moral-Language Use by U.S. Political Elites
Wang, S.-Y. N., & Inbar, Y. (2020) · Psychological Science · doi:10.1177/0956797620960397 · Cited by 67
Pyrolysing poultry litter reduces N2O and CO2 fluxes
ZWIETEN, L. V., Kimber, S. W. L., Morris, S., Singh, B. P., Grace, P., Scheer, C., Rust, J., Downie, A. E., & Cowie, A. (2013) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2013.02.054 · Cited by 70

Authors: Verbyla, A. P., Cullis, B. R., Kenward, M. G., & Welham, S. J.
Affiliations: University of Adelaide and South Australian Research and Development Institute , Glen Osmond , Australia
New South Wales Agriculture , Wagga Wagga , Australia
University of Kent (GB)
Rothamsted Research (GB)
ORCID: Cullis, Brian R.
Kenward, Michael G.
Venue: Journal of the Royal Statistical Society Series C: Applied Statistics
Publisher: Oxford University Press (OUP)
Published: 1999-09-01
Volume: 48
Issue: 3
Pages: 269-311
ISSN: 0035-9254 (ISSN-L)
1467-9876
DOI: 10.1111/1467-9876.00154
OpenAlex: W2153287720
Cited by: 570
References: 73
Citations by year: 2026: 3 · 2025: 2 · 2024: 6 · 2023: 11 · 2022: 13 · 2021: 12 · 2020: 23 · 2019: 22 · 2018: 11 · 2017: 20 · 2016: 24 · 2015: 29 · 2014: 37 · 2013: 29 · 2012: 25
FWCI: 26.38
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 C (Applied Statistics) (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
Kent Academic Repository (University of Kent) (submittedVersion)
Journal of the Royal Statistical Society Series A (Statistics in Society)
Funders: Grains Research and Development Corporation
University of Adelaide
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 › Agronomy and Crop Science › Agriculture, Soil, Plant Science
Keywords: smoothing splines, longitudinal data, designed experiments, linear mixed models, random coefficients, heteroscedasticity, serial correlation, cubic spline smoothing, seeding rate, spatial variability, fixed and random effects

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.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

The Analysis of Designed Experiments and Longitudinal Data by Using Smoothing Splines

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks