A survey of cross-validation procedures for model selection

@article{Arlot2010ASO,
  title={A survey of cross-validation procedures for model selection},
  author={Sylvain Arlot and Alain Celisse},
  journal={Statistics Surveys},
  year={2010},
  volume={4},
  pages={40-79}
}
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… Expand
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References

SHOWING 1-10 OF 220 REFERENCES
Linear Model Selection by Cross-validation
We consider the problem of model selection in the classical regression model based on cross-validation with an additional penalty term for penalizing overfitting.Under a given assumption,the newExpand
Unified Cross-Validation Methodology For Selection Among Estimators and a General Cross-Validated Adaptive Epsilon-Net Estimator: Finite Sample Oracle Inequalities and Examples
In Part I of this article we propose a general cross-validation criterian for selecting among a collection of estimators of a particular parameter of interest based on n i.i.d. observations. It isExpand
Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation
Abstract We construct a prediction rule on the basis of some data, and then wish to estimate the error rate of this rule in classifying future observations. Cross-validation provides a nearlyExpand
Model Selection Via Multifold Cross Validation
In model selection, it is known that the simple leave one out cross validation method is apt to select overfitted models. In an attempt to remedy this problem, we consider two notions of multi-foldExpand
Linear Model Selection by Cross-validation
Abstract We consider the problem of selecting a model having the best predictive ability among a class of linear models. The popular leave-one-out cross-validation method, which is asymptoticallyExpand
An alternative method of cross-validation for the smoothing of density estimates
Cross-validation with Kullback-Leibler loss function has been applied to the choice of a smoothing parameter in the kernel method of density estimation. A framework for this problem is constructedExpand
Robust Linear Model Selection by Cross-Validation
TLDR
A robust algorithm for model selection in regression models using Shao's cross-validation methods for choice of variables as a starting point is provided, demonstrating a substantial improvement in choosing the correct model in the presence of outliers with little loss of efficiency at the normal model. Expand
CONSISTENCY OF CROSS VALIDATION FOR COMPARING REGRESSION PROCEDURES
Theoretical developments on cross validation (CV) have mainly focused on selecting one among a list of finite-dimensional models (e.g., subset or order selection in linear regression) or selecting aExpand
On the use of cross-validation to assess performance in multivariate prediction
We describe a Monte Carlo investigation of a number of variants of cross-validation for the assessment of performance of predictive models, including different values of k in leave-k-outExpand
A local cross-validation algorithm
The usuall form of cross-validation is global in character, and is designed to estimate a density in some "average" sense over its entire support. In this paper we present a local version ofExpand
...
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