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A survey of cross-validation procedures for model selection

2010; American Statistical Association; Volume: 4; Issue: none Linguagem: Inglês

10.1214/09-ss054

1935-7516

Sylvain Arlot, Alain Célisse,

Statistical Methods and Bayesian Inference

Used to estimate the risk of an estimator or to perform model selection, cross-validation is a widespread strategy because of its simplicity and its apparent universality. Many results exist on the model selection performances of cross-validation procedures. This survey intends to relate these results to the most recent advances of model selection theory, with a particular emphasis on distinguishing empirical statements from rigorous theoretical results. As a conclusion, guidelines are provided for choosing the best cross-validation procedure according to the particular features of the problem in hand.