Corrected version of \(AIC\) for selecting multivariate normal linear regression models in a general nonnormal case

DOI10.1016/j.jmva.2005.06.005zbMath1089.62059OpenAlexW2058934055MaRDI QIDQ2493133

Hirokazu Yanagihara

Publication date: 9 June 2006

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

Full work available at URL: https://doi.org/10.1016/j.jmva.2005.06.005

zbMATH Keywords

robustnessbias reductionKullback--Leibler informationmodel misspecificationnormality assumptionselection of variablespredicted residualsJackknife methodinfluence of non-normality

Mathematics Subject Classification ID

Multivariate analysis (62H99) Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05)

Related Items (10)

Iterative Bias Correction of the Cross-Validation Criterion ⋮ Bias correction of cross-validation criterion based on Kullback-Leibler information under a general condition ⋮ Asymptotic biases of information and cross-validation criteria under canonical parametrization ⋮ An \(\ell_{2,0}\)-norm constrained matrix optimization via extended discrete first-order algorithms ⋮ Estimates of low bias for the multivariate normal ⋮ Calibration with low bias ⋮ Second-order bias-corrected AIC in multivariate normal linear models under non-normality ⋮ Jackknife bias correction of the AIC for selecting variables in canonical correlation analysis under model misspecification ⋮ Bias-Corrected AIC for Selecting Variables in Poisson Regression Models ⋮ A consistency property of the AIC for multivariate linear models when the dimension and the sample size are large

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