Bias Corrections of some Criteria for Selecting Multivariate Linear Models in a General Nonnormal Case

DOI10.1080/01966324.2005.10737651zbMath1151.62338OpenAlexW2088979904WikidataQ58244489 ScholiaQ58244489MaRDI QIDQ3520977

Hirokazu Yanagihara, Yasunori Fujikoshi, Hirofumi Wakaki

Publication date: 26 August 2008

Published in: American Journal of Mathematical and Management Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/01966324.2005.10737651

zbMATH Keywords

bootstrapasymptotic expansionAICBICmodel misspecificationmultivariate linear regression modelnonnormalityMallows' \(C_p\)selection of variablesTIC

Mathematics Subject Classification ID

Related Items (9)

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 ⋮ Consistent Bayesian information criterion based on a mixture prior for possibly high‐dimensional multivariate linear regression models ⋮ Selection of model selection criteria for multivariate ridge regression ⋮ Second-order bias-corrected AIC in multivariate normal linear models under non-normality ⋮ An unbiased \(C_p\) criterion for multivariate ridge regression ⋮ Jackknife bias correction of the AIC for selecting variables in canonical correlation analysis under model misspecification ⋮ Corrected version of \(AIC\) for selecting multivariate normal linear regression models in a general nonnormal case ⋮ A consistency property of the AIC for multivariate linear models when the dimension and the sample size are large

Cites Work

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• Further analysis of the data by Akaike's information criterion and the finite corrections
• Information Criteria for Discriminating Among Alternative Regression Models
• Modified AIC and Cp in multivariate linear regression
• Measures of multivariate skewness and kurtosis with applications

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