Cited by 1,103Statistical Methods and InferenceStatistical Methods and Bayesian InferenceStatistical Methods and Applications

The DOI matches its Crossref record; 116 of the 117 references it cites were found in a registry or an index.

Model Uncertainty, Data Mining and Statistical Inference

Chatfield, Chris·1995·Journal of the Royal Statistical Society. Series A (Statistics in Society)

DOI 10.2307/2983440

Open this paper in CoChat — free account

Abstract

This paper takes a broad, pragmatic view of statistical inference to include all aspects of model formulation. The estimation of model parameters traditionally assumes that a model has a prespecified known form and takes no account of possible uncertainty regarding the model structure. This implicitly assumes the existence of a 'true' model, which many would regard as a fiction. In practice model uncertainty is a fact of life and likely to be more serious than other sources of uncertainty which have received far more attention from statisticians. This is true whether the model is specified on subject-matter grounds or, as is increasingly the case, when a model is formulated, fitted and checked on the same data set in an iterative, interactive way. Modern computing power allows a large number of models to be considered and data-dependent specification searches have become the norm in many areas of statistics. The term data mining may be used in this context when the analyst goes to great lengths to obtain a good fit. This paper reviews the effects of model uncertainty, such as too narrow prediction intervals, and the non-trivial biases in parameter estimates which can follow data-based modelling. Ways of assessing and overcoming the effects of model uncertainty are discussed, including the use of simulation and resampling methods, a Bayesian model averaging approach and collecting additional data wherever possible. Perhaps the main aim of the paper is to ensure that statisticians are aware of the problems and start addressing the issues even if there is no simple, general theoretical fix.

Integrity signals

Registry

Crossref · 10.2307/2983440

Cites

116 of 116 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500
Book Review: Time Series Analysis: Forecasting and Control
Geurts, M. D., Box, G. E. P., & Jenkins, G. M. (1977) · Journal of Marketing Research · doi:10.2307/3150485 · Cited by 19,410
Statistical Methods for Research Workers
Fisher, R. A. (1945) · American Journal of Clinical Pathology · doi:10.1093/ajcp/15.11.552b · Cited by 10,883
The Logic of Scientific Discovery
Bhaskar, R. (2013) · Routledge eBooks · doi:10.4324/9780203090732-9 · Cited by 7,765
The Advanced Theory of Statistics.
Clarke, G. M., Kendall, M. G., & Stuart, A. (1978) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2344782 · Cited by 6,727
A leisurely look at the bootstrap, the jackknife, and cross-validation
Efron, B., & Gong, G. (1983) · The American Statistician · doi:10.1080/00031305.1983.10483087 · Cited by 3,229
Science and Statistics
Box, G. E. P. (1976) · Journal of the American Statistical Association · doi:10.1080/01621459.1976.10480949 · Cited by 2,114
Bayesian Forecasting and Dynamic Models
West, M., & Harrison, J. (1997) · Springer eBooks · doi:10.1007/b98971 · Cited by 1,946
Cross-Validation of Regression Models
Picard, R. R., & Cook, R. D. (1984) · Journal of the American Statistical Association · doi:10.1080/01621459.1984.10478083 · Cited by 1,551
Combining forecasts: A review and annotated bibliography
(1990) · Long Range Planning · doi:10.1016/0024-6301(90)90316-v · Cited by 1,508

Flagged first, then by citations

Show all 117 references

1 of the 117 references couldn’t be matched to a known paper.

Sorted by citations

Collinearity: a review of methods to deal with it and a simulation study evaluating their performance
Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., Marquéz, J. R. G., Gruber, B., Lafourcade, B., Leitão, P. J., et al. (2013) · Ecography · doi:10.1111/j.1600-0587.2012.07348.x · Cited by 9,998
A review of methods for the assessment of prediction errors in conservation presence/absence models
FIELDING, A. H., & BELL, J. F. (1997) · Environmental Conservation · doi:10.1017/s0376892997000088 · Cited by 6,956
Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration
Moons, K. G. M., Altman, D. G., Reitsma, J. B., Ioannidis, J. P. A., Macaskill, P., Steyerberg, E. W., Vickers, A. J., Ransohoff, D. F., & Collins, G. S. (2015) · Annals of Internal Medicine · doi:10.7326/m14-0698 · Cited by 5,147
Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors
Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999) · Statistical Science · doi:10.1214/ss/1009212519 · Cited by 4,118
Model Selection and Model Averaging in Phylogenetics: Advantages of Akaike Information Criterion and Bayesian Approaches Over Likelihood Ratio Tests
Posada, D., & Buckley, T. R. (2004) · Systematic Biology · doi:10.1080/10635150490522304 · Cited by 3,927
Internal validation of predictive models
Steyerberg, E. W., Harrell, F. E., Borsboom, G. J. J. M., Eijkemans, M. J. C., Vergouwe, Y., & Habbema, J. D. F. (2001) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(01)00341-9 · Cited by 2,746
Evaluating resource selection functions
Boyce, M. S., Vernier, P. R., Nielsen, S. E., & Schmiegelow, F. K. A. (2002) · Ecological Modelling · doi:10.1016/s0304-3800(02)00200-4 · Cited by 2,628
A brief introduction to mixed effects modelling and multi-model inference in ecology
Harrison, X. A., Donaldson, L., Correa-Cano, M. E., Evans, J., Fisher, D. N., Goodwin, C. E. D., Robinson, B. S., Hodgson, D. J., & Inger, R. (2018) · PeerJ · doi:10.7717/peerj.4794 · Cited by 2,094
POSTERIOR PREDICTIVE ASSESSMENT OF MODEL FITNESS VIA REALIZED DISCREPANCIES
Gelman, A., Meng, X., & Stern, H. S. (1996) · Cited by 1,982
A Reality Check for Data Snooping
White, H. (2000) · Econometrica · doi:10.1111/1468-0262.00152 · Cited by 1,851
Assessment and Propagation of Model Uncertainty
Draper, D. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02015.x · Cited by 1,599
Why Most Discovered True Associations Are Inflated
Ioannidis, J. P. A. (2008) · Epidemiology · doi:10.1097/ede.0b013e31818131e7 · Cited by 1,495
Model Selection: An Integral Part of Inference
Buckland, S. T., Burnham, K. P., & Augustin, N. H. (1997) · Biometrics · doi:10.2307/2533961 · Cited by 1,717
Handbook of Economic Forecasting
(2013) · Handbook of economic forecasting · doi:10.1016/b978-0-444-53683-9.00023-2 · Cited by 1,290
A meta‐analysis of context‐dependency in plant response to inoculation with mycorrhizal fungi
Hoeksema, J. D., Chaudhary, V. B., Gehring, C. A., Johnson, N. C., Karst, J., Koide, R. T., Pringle, A., Zabinski, C. A., Bever, J. D., Moore, J. C., et al. (2010) · Ecology Letters · doi:10.1111/j.1461-0248.2009.01430.x · Cited by 1,208
Cryptic multiple hypotheses testing in linear models: overestimated effect sizes and the winner's curse
Forstmeier, W., & Schielzeth, H. (2010) · Behavioral Ecology and Sociobiology · doi:10.1007/s00265-010-1038-5 · Cited by 1,154
Using Narrative in Social Research
Elliott, J. (2005) · SAGE Publications Ltd eBooks · doi:10.4135/9780857020246 · Cited by 1,114
The use of fractional polynomials to model continuous risk variables in epidemiology
Royston, P., Ambler, G., & Sauerbrei, W. (1999) · International Journal of Epidemiology · doi:10.1093/ije/28.5.964 · Cited by 1,066
Methods and uncertainties in bioclimatic envelope modelling under climate change
Heikkinen, R. K., Luoto, M., Araújo, M. B., Virkkala, R., Thuiller, W., & Sykes, M. T. (2006) · Progress in Physical Geography Earth and Environment · doi:10.1177/0309133306071957 · Cited by 1,016
Exponential smoothing: The state of the art—Part II
Gardner, E. S. (2006) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2006.03.005 · Cited by 954
A calibration hierarchy for risk models was defined: from utopia to empirical data
Van Calster, B., Nieboer, D., Vergouwe, Y., De Cock, B., Pencina, M. J., & Steyerberg, E. W. (2016) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2015.12.005 · Cited by 910
Statistical strategies for avoiding false discoveries in metabolomics and related experiments
Broadhurst, D., & Kell, D. B. (2006) · Metabolomics · doi:10.1007/s11306-006-0037-z · Cited by 857
Bayesian inference in ecology
Ellison, A. M. (2004) · Ecology Letters · doi:10.1111/j.1461-0248.2004.00603.x · Cited by 791
Prognostic modelling with logistic regression analysis: a comparison of selection and estimation methods in small data sets
Steyerberg, E. W., Eijkemans, M. J. C., Harrell, F. E., & Habbema, J. D. F. (2000) · Statistics in Medicine · doi:10.1002/(sici)1097-0258(20000430)19:8 · Cited by 789
Sensitivity and specificity of information criteria
(2019) · doi:10.1093/bib/bbz016 · Cited by 718
Global Change and Human Vulnerability to Vector-Borne Diseases
Sutherst, R. W. (2004) · Clinical Microbiology Reviews · doi:10.1128/cmr.17.1.136-173.2004 · Cited by 730
League Tables and Their Limitations: Statistical Issues in Comparisons of Institutional Performance
Goldstein, H., & Spiegelhalter, D. J. (1996) · Journal of the Royal Statistical Society. Series A (Statistics in Society) · doi:10.2307/2983325 · Cited by 1,069
Linear regression and the normality assumption
Schmidt, A. F., & Finan, C. (2017) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2017.12.006 · Cited by 702
Frequentist Model Average Estimators
Hjort, N. L., & Claeskens, G. (2003) · Journal of the American Statistical Association · doi:10.1198/016214503000000828 · Cited by 689
The Multi-dimensional Analysis of Social Exclusion
Levitas, R., Pantazis, C., Fahmy, E., Gordon, D., Lloyd, E., & Patsios, D. (2007) · UEL Research Repository (University of East London) · Cited by 671
Marine reserves: size and age do matter
Claudet, J., Osenberg, C. W., Benedetti‐Cecchi, L., Domenici, P., García‐Charton, J. A., Pérez‐Ruzafa, Á., Badalamenti, F., Bayle-Sempere, J. T., Brito, A., Bulleri, F., et al. (2008) · Ecology Letters · doi:10.1111/j.1461-0248.2008.01166.x · Cited by 691
Assessing transferability of ecological models: an underappreciated aspect of statistical validation
Wenger, S. J., & Olden, J. D. (2012) · Methods in Ecology and Evolution · doi:10.1111/j.2041-210x.2011.00170.x · Cited by 681
Substantial effective sample sizes were required for external validation studies of predictive logistic regression models
Vergouwe, Y., Steyerberg, E. W., Eijkemans, M. J. C., & Habbema, J. D. F. (2005) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2004.06.017 · Cited by 636
Econometrics: Alchemy or Science?
Hendry, D. F. (2000) · doi:10.1093/0198293542.001.0001 · Cited by 635
Internal and external validation of predictive models: A simulation study of bias and precision in small samples
Steyerberg, E. W., Bleeker, S. E., Moll, H. A., Grobbee, D. E., & Moons, K. G. M. (2003) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(03)00047-7 · Cited by 611
Preoperative prediction of severe postoperative pain
Kalkman, J. C., Visser, K., Moen, J., Bonsel, J. G., Grobbee, E. D., & Moons, M. K. G. (2003) · Pain · doi:10.1016/s0304-3959(03)00252-5 · Cited by 616
Practical identifiability analysis of large environmental simulation models
Brun, R., Reichert, P., & Künsch, H. R. (2001) · Water Resources Research · doi:10.1029/2000wr900350 · Cited by 609
Measurements and analysis of end-to-end Internet dynamics
Lawrence Berkeley National Lab. (LBNL), B., & Paxson, V. (1997) · doi:10.2172/551971 · Cited by 572
The role of sensitivity analysis in ecological modelling
Cariboni, J., Gatelli, D., Liška, R., & Saltelli, A. (2007) · Ecological Modelling · doi:10.1016/j.ecolmodel.2005.10.045 · Cited by 564
Prognostic Modeling with Logistic Regression Analysis
Steyerberg, E. W., Eijkemans, M. J. C., Harrell, F. E., & Habbema, J. D. F. (2001) · Medical Decision Making · doi:10.1177/0272989x0102100106 · Cited by 559
CLIMATE, HABITAT QUALITY, AND FITNESS IN NORTHERN SPOTTED OWL POPULATIONS IN NORTHWESTERN CALIFORNIA
Franklin, A. B., Anderson, D. R., Gutiérrez, R. J., & Burnham, K. P. (2000) · Ecological Monographs · doi:10.1890/0012-9615(2000)070[0539:chqafi]2.0.co;2 · Cited by 535
Simulation of winter wheat yield and its variability in different climates of Europe: A comparison of eight crop growth models
Palosuo, T., Kersebaum, K. C., Angulo, C., Hlavinka, P., Moriondo, M., Olesen, J. E., Patil, R. H., Ruget, F., Rumbaur, C., Takáč, J., et al. (2011) · European Journal of Agronomy · doi:10.1016/j.eja.2011.05.001 · Cited by 512
Stepwise Selection in Small Data Sets A Simulation Study of Bias in Logistic Regression Analysis
Steyerberg, E. W. (1999) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(99)00103-1 · Cited by 489
A Bayesian network of eutrophication models for synthesis, prediction, and uncertainty analysis
Borsuk, M. E., Stow, C. A., & Reckhow, K. H. (2003) · Ecological Modelling · doi:10.1016/j.ecolmodel.2003.08.020 · Cited by 494
Data mining reconsidered: encompassing and the general‐to‐specific approach to specification search
Hoover, K. D., & Perez, S. J. (1999) · Econometrics Journal · doi:10.1111/1368-423x.00025 · Cited by 488
An Introduction to Model Selection
Zucchini, W. (2000) · Journal of Mathematical Psychology · doi:10.1006/jmps.1999.1276 · Cited by 466
Palgrave Handbook of Econometrics
(2009) · Palgrave Macmillan UK eBooks · doi:10.1057/9780230244405 · Cited by 462
Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird
Manel, S., Dias, J.-M., & Ormerod, S. J. (1999) · Ecological Modelling · doi:10.1016/s0304-3800(99)00113-1 · Cited by 436
Serial Correlation as a Convenient Simplification, not a Nuisance: A Comment on a Study of the Demand for Money by the Bank of England
Hendry, D. F., & Mizon, G. E. (2000) · doi:10.1093/0198293542.003.0007 · Cited by 430
Improving data analysis in herpetology: using Akaike's Information Criterion (AIC) to assess the strength of biological hypotheses
Mazerolle, M. J. (2006) · Amphibia-Reptilia · doi:10.1163/156853806777239922 · Cited by 431
Sampling for validation of digital soil maps
Brus, D. J., Kempen, B., & Heuvelink, G. B. M. (2011) · European Journal of Soil Science · doi:10.1111/j.1365-2389.2011.01364.x · Cited by 416
Applications and limitations of museum data for conservation and ecology, with particular attention to species distribution models
Newbold, T. (2010) · Progress in Physical Geography Earth and Environment · doi:10.1177/0309133309355630 · Cited by 404
Model weights and the foundations of multimodel inference.
Link, W., & Barker, R. (2006) · Ecology · doi:10.1890/0012-9658(2006)87[2626:mwatfo]2.0.co;2 · Cited by 421
Ecology of butterflies in Europe
(2010) · Choice Reviews Online · doi:10.5860/choice.47-5648 · Cited by 399
Rapid identification of urinary tract infection bacteria using hyperspectral whole-organism fingerprinting and artificial neural networks
Goodacre, R., Timmins, É. M., Burton, R., Kaderbhai, N., Woodward, A. M., Kell, D. B., & Rooney, P. J. (1998) · Microbiology · doi:10.1099/00221287-144-5-1157 · Cited by 384
The Focused Information Criterion
Claeskens, G., & Hjort, N. L. (2003) · Journal of the American Statistical Association · doi:10.1198/016214503000000819 · Cited by 381
On modeling
Novikov, D. S., Kiselev, V. G., & Jespersen, S. N. (2018) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27101 · Cited by 362
Information theory and hypothesis testing: a call for pluralism
Stephens, P., Buskirk, S., Hayward, G., & del Rio, C. (2005) · Journal of Applied Ecology · doi:10.1111/j.1365-2664.2005.01002.x · Cited by 317
TEMPORAL AND SPATIAL VARIATION IN ENVIRONMENTAL IMPACT ASSESSMENT
Stewart‐Oaten, A., & Bence, J. R. (2001) · Ecological Monographs · doi:10.1890/0012-9615(2001)071[0305:tasvie]2.0.co;2 · Cited by 358
False Discovery Rates in PET and CT Studies with Texture Features: A Systematic Review
Chalkidou, A., O'Doherty, M., & Marsden, P. (2015) · PLoS ONE · doi:10.1371/journal.pone.0124165 · Cited by 338
A simulation study of artificial neural networks for nonlinear time-series forecasting
Zhang, G. P., Patuwo, B. E., & Hu, M. Y. (2001) · Computers & Operations Research · doi:10.1016/s0305-0548(99)00123-9 · Cited by 342
Modelling fish growth: Model selection, multi-model inference and model selection uncertainty
Katsanevakis, S. (2006) · Fisheries Research · doi:10.1016/j.fishres.2006.07.002 · Cited by 334
Practical Regression and Anova using R
Faraway, J. J. (2002) · Cited by 331
Quantitative Methods for Modeling Species Habitat: Comparative Performance and an Application to Australian Plants
Elith, J. (2006) · Springer eBooks · doi:10.1007/0-387-22648-6_4 · Cited by 326
Model Averaging and Its Use in Economics
Steel, M. F. J. (2020) · Journal of Economic Literature · doi:10.1257/jel.20191385 · Cited by 321
The continuing challenges of testing species distribution models
Vaughan, I., & Ormerod, S. (2005) · Journal of Applied Ecology · doi:10.1111/j.1365-2664.2005.01052.x · Cited by 341
Alternative methods for predicting species distribution: an illustration with Himalayan river birds
Manel, S., Dias, J.-M., Buckton, S. T., & Ormerod, S. J. (1999) · Journal of Applied Ecology · doi:10.1046/j.1365-2664.1999.00440.x · Cited by 313
The Use of Resampling Methods to Simplify Regression Models in Medical Statistics
Sauerbrei, W. (1999) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/1467-9876.00155 · Cited by 310
Fauna habitat modelling and mapping: A review and case study in the Lower Hunter Central Coast region of NSW
Wintle, B. A., Elith, J., & Potts, J. (2005) · Austral Ecology · doi:10.1111/j.1442-9993.2005.01514.x · Cited by 306
Species distribution models rarely predict the biology of real populations
Lee‐Yaw, J. A., McCune, J. L., Pironon, S., & Sheth, S. N. (2021) · Ecography · doi:10.1111/ecog.05877 · Cited by 326
Statistical and integrative system-level analysis of DNA methylation data
Teschendorff, A. E., & Relton, C. L. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2017.86 · Cited by 297
Marine reserves: Fish life history and ecological traits matter
Claudet, J., Osenberg, C. W., Domenici, P., Badalamenti, F., Milazzo, M., FALCÓN, J. M., Bertocci, I., Benedetti‐Cecchi, L., García‐Charton, J. A., Goñi, R., et al. (2010) · Ecological Applications · doi:10.1890/08-2131.1 · Cited by 295
Heavy tail modeling and teletraffic data: special invited paper
Resnick, S. I. (1997) · The Annals of Statistics · doi:10.1214/aos/1069362376 · Cited by 288
Forecasting and operational research: a review
Fildes, R., Nikolopoulos, K., Crone, S. F., & Syntetos, A. A. (2008) · Journal of the Operational Research Society · doi:10.1057/palgrave.jors.2602597 · Cited by 287
The Use of Bayesian Model Averaging to Better Represent Uncertainty in Ecological Models
Wintle, B. A., Mccarthy, M. A., Volinsky, C. T., & Kavanagh, R. P. (2003) · Conservation Biology · doi:10.1111/j.1523-1739.2003.00614.x · Cited by 286
Modelling fish growth: multi‐model inference as a better alternative to a priori using von Bertalanffy equation
Katsanevakis, S., & Maravelias, C. D. (2008) · Fish and Fisheries · doi:10.1111/j.1467-2979.2008.00279.x · Cited by 278
Real longitudinal data analysis for real people: Building a good enough mixed model
Cheng, J., Edwards, L. J., Maldonado‐Molina, M. M., Komro, K. A., & Muller, K. E. (2009) · Statistics in Medicine · doi:10.1002/sim.3775 · Cited by 276
Statistical Foundations for Model-Based Adjustments
Greenland, S., & Pearce, N. E. (2015) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-031914-122559 · Cited by 274
Mapping epistemic uncertainties and vague concepts in predictions of species distribution
Elith, J., Burgman, M. A., & Regan, H. M. (2002) · Ecological Modelling · doi:10.1016/s0304-3800(02)00202-8 · Cited by 273
More on a Stochastic Asset Model for Actuarial Use
Wilkie, A. D. (1995) · British Actuarial Journal · doi:10.1017/s1357321700001331 · Cited by 269
Guidelines for Computer Modeling of Diabetes and Its Complications
American Diabetes Association Consensus Panel (2004) · Diabetes Care · doi:10.2337/diacare.27.9.2262 · Cited by 270
Cluster and Classification Techniques for the Biosciences
Fielding, A. H. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511607493 · Cited by 266
Variable selection and Bayesian model averaging in case‐control studies
Viallefont, V., Raftery, A. E., & Richardson, S. T. (2001) · Statistics in Medicine · doi:10.1002/sim.976 · Cited by 263
State of the art in selection of variables and functional forms in multivariable analysis—outstanding issues
initiative, F. T. O. T. S., Sauerbrei, W., Perperoglou, A., Schmid, M. C., Abrahamowicz, M., Becher, H., Binder, H., Dunkler, D., Harrell, F. E., Royston, P. J., et al. (2020) · Diagnostic and Prognostic Research · doi:10.1186/s41512-020-00074-3 · Cited by 270
An investigation of model selection criteria for neural network time series forecasting
Qi, M., & Zhang, G. P. (2001) · European Journal of Operational Research · doi:10.1016/s0377-2217(00)00171-5 · Cited by 258
Assessing community-based conservation projects: A systematic review and multilevel analysis of attitudinal, behavioral, ecological, and economic outcomes
Brooks, J. S., Waylen, K. A., & Mulder, M. B. (2013) · Environmental Evidence · doi:10.1186/2047-2382-2-2 · Cited by 283
Assessing the Value of Risk Predictions by Using Risk Stratification Tables
Janes, H., PEPE, M. S., & Gu, W. (2008) · Annals of Internal Medicine · doi:10.7326/0003-4819-149-10-200811180-00009 · Cited by 249
Comparison of Akaike information criterion and consistent Akaike information criterion for model selection and statistical inference from capture-recapture studies
Anderson, D. R., Burnham, K. P., & White, G. C. (1998) · Journal of Applied Statistics · doi:10.1080/02664769823250 · Cited by 311
Should scoring rules be based on odds ratios or regression coefficients?
Moons, K. G. M., Harrell, F. E., & Steyerberg, E. W. (2002) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(02)00453-5 · Cited by 241
Statistical Issues and Assumptions of Phylogenetic Generalized Least Squares
Mundry, R. (2014) · Springer eBooks · doi:10.1007/978-3-662-43550-2_6 · Cited by 236
Where Mathematics meets the Internet
Willinger, W., & Paxson, V. (1998) · Cited by 232
Incidence of renal dysfunction over 6 months in patients with chronic heart failure due to left ventricular systolic dysfunction: contributing factors and relationship to prognosis
de Silva, R., Nikitin, N. P., Witte, K. K., Rigby, A. S., Goode, K. M., Bhandari, S., Clark, A. L., & Cleland, J. G. F. (2005) · European Heart Journal · doi:10.1093/eurheartj/ehi696 · Cited by 227
Bayesian Model Averaging in Proportional Hazard Models: Assessing the Risk of a Stroke
Volinsky, C. T., Madigan, D., Raftery, A. E., & Kronmal, R. A. (1997) · Journal of the Royal Statistical Society Series C: Applied Statistics · doi:10.1111/1467-9876.00082 · Cited by 226
GemPy 1.0: open-source stochastic geological modeling and inversion
de la Varga, M., Schaaf, A., & Wellmann, F. (2019) · Geoscientific model development · doi:10.5194/gmd-12-1-2019 · Cited by 226
The significance and prediction of predation in marine fisheries
Bax, N. J. (1998) · ICES Journal of Marine Science · doi:10.1006/jmsc.1998.0350 · Cited by 225
Methods of Covariate Selection: Directed Acyclic Graphs and the Change-in-Estimate Procedure
Weng, H., Hsueh, Y.-H., Messam, L. L. M., & Hertz‐Picciotto, I. (2009) · American Journal of Epidemiology · doi:10.1093/aje/kwp035 · Cited by 223
COMPONENTS OF UNCERTAINTY IN SPECIES DISTRIBUTION ANALYSIS: A CASE STUDY OF THE GREAT GREY SHRIKE
Dormann, C. F., Purschke, O., Márquez, J. R. G., Lautenbach, S., & Schröder, B. (2008) · Ecology · doi:10.1890/07-1772.1 · Cited by 220
Multivariable Modeling Strategies
Harrell, F. E. (2015) · Springer Series in Statistics · doi:10.1007/978-3-319-19425-7_4 · Cited by 221
Models and model uncertainty in the context of risk analysis
Nilsen, T., & Aven, T. (2003) · Reliability Engineering & System Safety · doi:10.1016/s0951-8320(02)00239-9 · Cited by 221
COMBINING FORECASTING PROCEDURES: SOME THEORETICAL RESULTS
Yang, Y. (2004) · Econometric Theory · doi:10.1017/s0266466604201086 · Cited by 221

Authors: Chatfield, C.
Affiliations: University of Bath (GB)
Corresponding author: Chatfield, Chris
Venue: Journal of the Royal Statistical Society. Series A (Statistics in Society)
Publisher: Oxford University Press (OUP)
Published: 1995
Volume: 158
Issue: 3
Pages: 419
ISSN: 0964-1998 (ISSN-L)
DOI: 10.2307/2983440
OpenAlex: W2060050130
Cited by: 1,103
References: 117
Citations by year: 2026: 18 · 2025: 34 · 2024: 38 · 2023: 33 · 2022: 35 · 2021: 33 · 2020: 44 · 2019: 28 · 2018: 47 · 2017: 35 · 2016: 37 · 2015: 51 · 2014: 52 · 2013: 28 · 2012: 63
FWCI: 24.46
Type: journal article
Language: en
Open access: closed
Locations: Journal of the Royal Statistical Society Series A (Statistics in Society) (publishedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Keywords: model uncertainty, data mining, statistical inference, model selection, prediction intervals, estimation bias, Bayesian model averaging, resampling, simulation, model misspecification

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

Model Uncertainty, Data Mining and Statistical Inference

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks