%0 Journal Article %1 arlot2009survey %A Arlot, Sylvain %A Celisse, Alain %D 2009 %J Statistics Surveys %K statistics cross-validation machine_learning review %N 0 %P 40--79 %R 10.1214/09-ss054 %T A survey of cross-validation procedures for model selection %U http://dx.doi.org/10.1214/09-ss054 %V 4 %X 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.

@article{arlot2009survey, 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.}}, added-at = {2018-12-07T09:10:16.000+0100}, archiveprefix = {arXiv}, author = {Arlot, Sylvain and Celisse, Alain}, biburl = {https://www.bibsonomy.org/bibtex/256441c7ea477ff21fbc47a4afe470a1e/jpvaldes}, citeulike-article-id = {7538343}, citeulike-linkout-0 = {http://arxiv.org/abs/0907.4728}, citeulike-linkout-1 = {http://arxiv.org/pdf/0907.4728}, citeulike-linkout-2 = {http://dx.doi.org/10.1214/09-ss054}, day = 27, doi = {10.1214/09-ss054}, eprint = {0907.4728}, interhash = {b36dd0aa480b6c21064a358d78080095}, intrahash = {56441c7ea477ff21fbc47a4afe470a1e}, issn = {1935-7516}, journal = {Statistics Surveys}, keywords = {statistics cross-validation machine_learning review}, month = jul, number = 0, pages = {40--79}, posted-at = {2017-03-13 09:07:25}, priority = {4}, timestamp = {2018-12-07T09:39:56.000+0100}, title = {{A survey of cross-validation procedures for model selection}}, url = {http://dx.doi.org/10.1214/09-ss054}, volume = 4, year = 2009 }