Bias correction of cross-validation criterion based on Kullback-Leibler information under a general condition

DOI10.1016/j.jmva.2005.10.009zbMath1101.62047OpenAlexW2000626676MaRDI QIDQ855905

Hirokazu Yanagihara, Chieko Matsumoto, Tetsuji Tonda

Publication date: 7 December 2006

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2005.10.009

zbMATH Keywords

robustnessmodel selectioncross-validationbias correctionmodel misspecificationpredictive Kullback-Leibler informationweighted log-likelihood function

Mathematics Subject Classification ID

Related Items (8)

Iterative Bias Correction of the Cross-Validation Criterion ⋮ Information Measures in Biostatistics and Reliability Engineering ⋮ Asymptotic biases of information and cross-validation criteria under canonical parametrization ⋮ A class of cross-validatory model selection criteria ⋮ Estimating the Kullback–Liebler risk based on multifold cross‐validation ⋮ Estimation of prediction error by using \(K\)-fold cross-validation ⋮ A NEW FAMILY OF DIVERGENCE MEASURES FOR TESTS OF FIT ⋮ A computationally fast alternative to cross-validation in penalized Gaussian graphical models

Cites Work

• A family of estimators for multivariate kurtosis in a nonnormal linear regression model
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• Corrected versions of cross-validation criteria for selecting multivariate regression and growth curve models
• Corrected version of \(AIC\) for selecting multivariate normal linear regression models in a general nonnormal case
• Bias Corrections of some Criteria for Selecting Multivariate Linear Models in a General Nonnormal Case
• On Information and Sufficiency
• Maximum Likelihood Estimation of Misspecified Models
• A new look at the statistical model identification
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