Asymptotic optimality of full cross-validation for selecting linear regression models
type: Item
instance of: scholarly article
title: Asymptotic optimality of full cross-validation for selecting linear regression models
zbMATH Open document ID: 0947.62043
DOI: 10.1016/S0167-7152(99)00026-7
author: Bernd Droge
published in: Statistics \& Probability Letters
publication date: 2000-01-30T00:00:00Z
Mathematics Subject Classification ID: 62J05
zbMATH DE Number: 1395091
zbMATH Keywords: full cross-validation
zbMATH Keywords: prediction
zbMATH Keywords: model selection
zbMATH Keywords: cross-validation
zbMATH Keywords: asymptotic optimality
MaRDI profile type: Publication
cites work: Bootstrap and cross-validation estimates of the prediction error for linear regression models
cites work: Model Selection, Transformations and Variance Estimation in Nonlinear Regression
cites work: Q4871533
cites work: Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation
cites work: How Biased is the Apparent Error Rate of a Prediction Rule?
cites work: Asymptotic optimality for \(C_ p\), \(C_ L\), cross-validation and generalized cross-validation: Discrete index set
cites work: Asymptotic properties of criteria for selection of variables in multiple regression
cites work: Estimating the dimension of a model
cites work: An optimal selection of regression variables
cites work: Q4066417
cites work: Q3283298
description: scientific article; zbMATH DE number 1395091