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      A survey of cross-validation procedures for model selection

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          Abstract

          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.

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          Journal
          10.1214/09-SS054
          0907.4728

          Applications,Machine learning,Methodology,Statistics theory
          Applications, Machine learning, Methodology, Statistics theory

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