Asymptotic optimality of full cross-validation for selecting linear regression models

DOI10.1016/S0167-7152(99)00026-7zbMath0947.62043MaRDI QIDQ1962131

Bernd Droge

Publication date: 30 January 2000

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

zbMATH Keywords

predictionmodel selectioncross-validationasymptotic optimalityfull cross-validation

Mathematics Subject Classification ID

Linear regression; mixed models (62J05)

Related Items (1)

Model Selection, Transformations and Variance Estimation in Nonlinear Regression

Cites Work

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• Bootstrap and cross-validation estimates of the prediction error for linear regression models
• Asymptotic properties of criteria for selection of variables in multiple regression
• Asymptotic optimality for \(C_ p\), \(C_ L\), cross-validation and generalized cross-validation: Discrete index set
• Estimating the dimension of a model
• Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation
• How Biased is the Apparent Error Rate of a Prediction Rule?
• An optimal selection of regression variables
• Model Selection, Transformations and Variance Estimation in Nonlinear Regression

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