Title :

A survey of cross-validation procedures for model selection

Author(s) :

Arlot, Sylvain [Auteur]
Laboratoire d'informatique de l'école normale supérieure [LIENS]
Celisse, Alain [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]

Journal title :

Statistics Surveys

Pages :

40--79

Publisher :

Institute of Mathematical Statistics (IMS)

Publication date :

2010

ISSN :

1935-7516

English keyword(s) :

model selection
cross-validation
leave-one-out

HAL domain(s) :

Mathématiques [math]/Statistiques [math.ST]
Statistiques [stat]/Autres [stat.ML]
Statistiques [stat]/Théorie [stat.TH]
Statistiques [stat]/Applications [stat.AP]
Statistiques [stat]/Méthodologie [stat.ME]

English abstract : [en]

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 ...
Show more >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.Show less >

Language :

Anglais

Popular science :

Non

ANR Project :

DETECT: Nouvelles approches statistiques pour la vision artificielle et la bioinformatique

Comment :

Published in Statistics Surveys (2010) 4, 40-79

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL