Published in Statistics Surveys (2010) 4, 40-79International audienceUsed 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
We consider the problem of model (or variable) selection in the classical regression model based on ...
A model selection criterion is often formulated by constructing an approx-imately unbiased estimator...
Longitudinal models are commonly used for studying data collected on individuals repeatedly through ...
When selecting a classification algorithm to be applied to a particular problem, one has to simultan...
Appealing due to its universality, cross-validation is an ubiquitous tool for model tuning and selec...
This article gives a robust technique for model selection in regression models, an important aspect ...
Risk estimation is an important statistical question for the purposes of selecting a good estimator ...
This paper concerns a class of model selection criteria based on cross-validation techniques and est...
This paper studies V-fold cross-validation for model selection in least-squares density estimation. ...
We review accuracy estimation methods and compare the two most common methods crossvalidation and bo...
This paper studies V-fold cross-validation for model selection in least-squares density estimation. ...
L'objet de cette thèse est l'étude d'un certain type d'algorithmes de rééchantillonnage regroupés so...
Suppose that we observe a sample of independent and identically distributed realizations of a random...
This thesis will be concerned with application of a cross-validation criterion to the choice and as...
that leave-one-out cross-validation is not subject to the “no-free-lunch ” criticism. Despite this o...
We consider the problem of model (or variable) selection in the classical regression model based on ...
A model selection criterion is often formulated by constructing an approx-imately unbiased estimator...
Longitudinal models are commonly used for studying data collected on individuals repeatedly through ...
When selecting a classification algorithm to be applied to a particular problem, one has to simultan...
Appealing due to its universality, cross-validation is an ubiquitous tool for model tuning and selec...
This article gives a robust technique for model selection in regression models, an important aspect ...
Risk estimation is an important statistical question for the purposes of selecting a good estimator ...
This paper concerns a class of model selection criteria based on cross-validation techniques and est...
This paper studies V-fold cross-validation for model selection in least-squares density estimation. ...
We review accuracy estimation methods and compare the two most common methods crossvalidation and bo...
This paper studies V-fold cross-validation for model selection in least-squares density estimation. ...
L'objet de cette thèse est l'étude d'un certain type d'algorithmes de rééchantillonnage regroupés so...
Suppose that we observe a sample of independent and identically distributed realizations of a random...
This thesis will be concerned with application of a cross-validation criterion to the choice and as...
that leave-one-out cross-validation is not subject to the “no-free-lunch ” criticism. Despite this o...
We consider the problem of model (or variable) selection in the classical regression model based on ...
A model selection criterion is often formulated by constructing an approx-imately unbiased estimator...
Longitudinal models are commonly used for studying data collected on individuals repeatedly through ...