Second-order bias-corrected AIC in multivariate normal linear models under non-normality

DOI10.1002/cjs.10090zbMath1349.62213OpenAlexW2141724421MaRDI QIDQ3019147

Hirokazu Yanagihara, Ken-ichi Kamo, Tetsuji Tonda

Publication date: 27 July 2011

Published in: Canadian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/cjs.10090

zbMATH Keywords

Kullback-Leibler informationbias correctionAkaike's information criterionmodel misspecificationnormal assumptionselection of variablesoverspecified modelpredicted residual sum of squaresrobustness to non-normalityvariance of information criterion

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05) Statistical ranking and selection procedures (62F07) Statistical aspects of information-theoretic topics (62B10)

Related Items (4)

Iterative Bias Correction of the Cross-Validation Criterion ⋮ A class of cross-validatory model selection criteria ⋮ Jackknife bias correction of the AIC for selecting variables in canonical correlation analysis under model misspecification ⋮ A consistency property of the AIC for multivariate linear models when the dimension and the sample size are large

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