模型选择交叉验证程序的调查

IF 11 Q1 STATISTICS & PROBABILITY

Statistics Surveys Pub Date : 2009-07-27 DOI:10.1214/09-SS054

Sylvain Arlot, Alain Celisse

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引用次数: 3333

摘要

用于估计估计器的风险或执行模型选择，交叉验证是一种广泛的策略，因为它的简单性和明显的通用性。关于交叉验证方法的模型选择性能有很多研究结果。本调查旨在将这些结果与模型选择理论的最新进展联系起来，特别强调从严格的理论结果中区分经验陈述。作为结论，提供了根据手头问题的特定特征选择最佳交叉验证程序的指导方针。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A survey of cross-validation procedures for model selection

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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来源期刊

Statistics Surveys STATISTICS & PROBABILITY-

CiteScore

11.70

自引率

0.00%

发文量

期刊介绍： Statistics Surveys publishes survey articles in theoretical, computational, and applied statistics. The style of articles may range from reviews of recent research to graduate textbook exposition. Articles may be broad or narrow in scope. The essential requirements are a well specified topic and target audience, together with clear exposition. Statistics Surveys is sponsored by the American Statistical Association, the Bernoulli Society, the Institute of Mathematical Statistics, and by the Statistical Society of Canada.