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Model selection aims to identify a sufficiently well performing model that is possibly simpler than the most complex model among a pool of candidates.
Approximate leave-future-out cross-validation for Bayesian time series models
Bürkner, P.-C., Gabry, J., and Vehtari, A. (2020) · 1902
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Statistical estimates and transformed beta-variables
Blom, G. (1960) · 1960
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Expected values of normal order statistics
Harter, H. L. (1961) · 1961
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The choice of variables in multiple regression
Lindley, D. V. (1968) · 1968
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Cross-validatory choice and assessment of statistical predictions
Stone, M. (1974) · 1974
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The predictive sample reuse method with applications
Geisser, S. (1975) · 1975
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A predictive approach to model selection
Geisser, S. and Eddy, W. F. (1979) · 1979
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Information criteria for choice of regression models: A comment
Leamer, E. E. (1979) · 1979
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Algorithm AS 177: Expected normal order statistics (exact and approximate)
Royston, J. P. (1982) · 1982
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Analysis of hidden units in a layered network trained to classify sonar targets
Gorman, R. and Sejnowski, T. J. (1988) · 1988
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Classification of radar returns from the ionosphere using neural networks
Sigillito, V., Wing, S., Hutton, L. V., and Baker, K. (1989) · 1989
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Model determination using predictive distributions with implementation via sampling-based methods
Gelfand, A. E., Dey, D. K., and Chang, H. (1992) · 1992
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An introduction to the bootstrap
Efron, B. and Tibshirani, R. (1993) · 1993
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Variable selection via Gibbs sampling
George, E. I. and McCulloch, R. E. (1993) · 1993
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Linear model selection by cross-validation
Shao, J. (1993) · 1993
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Bayesian Theory
Bernardo, J. M. and Smith, A. F. M. (1994) · 1994
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Bayes factors
Kass, R. E. and Raftery, A. E. (1995) · 1995
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Predictive model selection
Laud, P. W. and Ibrahim, J. G. (1995) · 1995
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Model determination using sampling-based methods
Gelfand, A. E. (1996) · 1996
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Posterior predictive assessment of model fitness via realized discrepancies
Gelman, A., Xiao-Li, M., and Stern, H. S. (1996) · 1996
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Multivariate Bayesian variable selection and prediction
Brown, P. J., Vannucci, M., and Fearn, T. (1998) · 1998
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Model choice: a minimum posterior predictive loss approach
Gelfand, A. and Ghosh, S. K. (1998) · 1998
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Model choice in generalised linear models: a Bayesian approach via Kullback-Leibler projections
Goutis, C. (1998) · 1998
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Theory of probability
Jeffreys, H. (1998) · 1998
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Artificial Intelligence: A New Synthesis
Nilsson, N. J. (1998) · 1998
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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., and Volinsky, C. T. (1999) · 1999
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Bayesian model choice: What and why?
Key, J., Pericchi, L., and Smith, A. F. M. (1999) · 1999
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Stochastic search variable selection for log-linear models
Ntzoufras, I., Forster, J. J., and Dellaportas, P. (2000) · 2000
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Markov chain Monte Carlo methods for computing Bayes factors: A comparative review
Han, C. and Carlin, B. P. (2001) · 2001
Cited alongside, same era.
Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis
Harrell, F. E. (2001) · 2001
Cited alongside, same era.
SPECTF heart data
Krzysztof Cios, Lukasz Kurgan, L. G. (2001) · 2001
Cited alongside, same era.
A Bayesian approach to selecting covariates for prediction
Marriott, J. M., Spencer, N. M., and Pettitt, A. N. (2001) · 2001
Cited alongside, same era.
Selection bias in gene extraction on the basis of microarray gene-expression data
Ambroise, C. and McLachlan, G. J. (2002) · 2002
Cited alongside, same era.
Model Selection and Multi-Model Inference: A Practical Information-Theoretic Approach
Burnham, K. P. and Anderson, D. R. (2002) · 2002
Understanding predictive information criteria for Bayesian models
Gelman, A., Hwang, J., and Vehtari, A. (2014) · 2014
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Bayesian variable selection with shrinking and diffusing priors
Narisetty, N. N. and He, X. (2014) · 2014
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Projective covariate selection
Robert, C. (2014) · 2014
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Highly informative marker sets consisting of genes with low individual degree of differential expression
Galatenko, V. V., Shkurnikov, M. Y., Samatov, T. R., Galatenko, A. V., Mityakina, I. A., Kaprin, A. D., Schumacher, U., and Tonevitsky, A. G. (2015) · 2015
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Difficulty of selecting among multilevel models using predictive accuracy
Wang, W. and Gelman, A. (2015) · 2015
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brms: An R package for Bayesian multilevel models using Stan
Bürkner, P.-C. (2017) · 2017
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Cited alongside, same era.
A data-driven software tool for enabling cooperative information sharing among police departments
Redmond, M. and Baveja, A. (2002) · 2002
Cited alongside, same era.
Bayesian measures of model complexity and fit
Spiegelhalter, D. J., Best, N. G., Carlin, B. P., and van der Linde, A. (2002) · 2002
Cited alongside, same era.
Bayesian model assessment and comparison using cross-validation predictive densities
Vehtari, A. and Lampinen, J. (2002) · 2002
Cited alongside, same era.
Variable selection in qualitative models via an entropic explanatory power
Dupuis, J. A. and Robert, C. P. (2003) · 2003
Cited alongside, same era.
When are Bayesian model probabilities overconfident?
Oelrich, O., Ding, S., Magnusson, M., Vehtari, A., and Villani, M. (2020) · 2003
Cited alongside, same era.
Discussion: Performance of Bayesian model averaging
Raftery, A. E. and Zheng, Y. (2003) · 2003
Cited alongside, same era.
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The prior can often only be understood in the context of the likelihood
Gelman, A., Simpson, D., and Betancourt, M. (2017) · 2017
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Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
Vehtari, A., Gelman, A., and Gabry, J. (2017) · 2017
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Using stacking to average Bayesian predictive distributions (with discussion)
Yao, Y., Vehtari, A., Simpson, D., and Gelman, A. (2018) · 2018
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Bayesian Comparison of Latent Variable Models: Conditional Versus Marginal Likelihoods
Merkle, E. C., Furr, D., and Rabe-Hesketh, S. (2019) · 2019
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Projective inference in high-dimensional problems: Prediction and feature selection
Piironen, J., Paasiniemi, M., and Vehtari, A. (2020) · 2020
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Latent space projection predictive inference
Catalina, A., Bürkner, P., and Vehtari, A. (2021) · 2021
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Bayesian hierarchical stacking: Some models are (somewhere) useful
Yao, Y., Pirš, G., Vehtari, A., and Gelman, A. (2021) · 2021
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Parsimonious model selection using information theory: a modified selection rule
Yates, L. A., Richards, S. A., and Brook, B. W. (2021) · 2021
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I’m skeptical of that claim that “Cash aid to poor mothers increases brain activity in babies”
Gelman, A. (2022) · 2022
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Model averaging is asymptotically better than model selection for prediction
Le, T. M. and Clarke, B. S. (2022) · 2022
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Scholz, M. and Bürkner, P.-C. (2022) · 2022
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The impact of a poverty reduction intervention on infant brain activity
Troller-Renfree, S. V., Costanzo, M. A., Duncan, G. J., Magnuson, K., Gennetian, L. A., Yoshikawa, H., Halpern-Meekin, S., Fox, N. A., and Noble, K. G. (2022) · 2022
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Pareto Smoothed Importance Sampling
Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2022) · 2022
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Bayesian regression using a prior on the model fit: The R2-D2 shrinkage prior
Zhang, Y. D., Naughton, B. P., Bondell, H. D., and Reich, B. J. (2022) · 2022
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Intuitive joint priors for Bayesian linear multilevel models: The R2D2M2 prior
Aguilar, J. E. and Bürkner, P.-C. (2023) · 2023
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Cross-validatory model selection for Bayesian autoregressions with exogenous regressors
Cooper, A., Simpson, D., Kennedy, L., Forbes, C., and Vehtari, A. (2023) · 2023
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Meta-uncertainty in bayesian model comparison
Schmitt, M., Radev, S. T., and Bürkner, P.-C. (2023) · 2023
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loo: Efficient leave-one-out cross-validation and waic for bayesian models
Vehtari, A., Gabry, J., Magnusson, M., Yao, Y., Bürkner, P.-C., Paananen, T., and Gelman, A. (2023) · 2023
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Projection predictive variable selection for discrete response families with finite support
Weber, F. and Vehtari, A. (2023) · 2023
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Locking and quacking: Stacking Bayesian model predictions by log-pooling and superposition
Yao, Y., Carvalho, L. M., Mesquita, D., and McLatchie, Y. (2023) · 2023
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Advances in projection predictive inference
McLatchie, Y., Rögnvaldsson, S., Weber, F., and Vehtari, A. (2024) · 2024
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On over-fitting in model selection and subsequent selection bias in performance evaluation
Cawley, G. C. and Talbot, N. L. C. (2010) · 2079
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