Corrected versions of cross-validation criteria for selecting multivariate regression and growth curve models

DOI10.1007/BF02517806zbMath1047.62051OpenAlexW2055763649MaRDI QIDQ1881417

Takafumi Noguchi, Yasunori Fujikoshi, Megu Ohtaki, Hirokazu Yanagihara

Publication date: 5 October 2004

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02517806

zbMATH Keywords

model selectionriskgrowth curve modelsmultivariate regression modelscorrected versionsCV criterion

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05)

Related Items (9)

Bias correction of cross-validation criterion based on Kullback-Leibler information under a general condition ⋮ A family of estimators for multivariate kurtosis in a nonnormal linear regression model ⋮ 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 ⋮ Second-order bias-corrected AIC in multivariate normal linear models under non-normality ⋮ An unbiased \(C_p\) criterion for multivariate ridge regression ⋮ Corrected version of \(AIC\) for selecting multivariate normal linear regression models in a general nonnormal case ⋮ Bias-Corrected AIC for Selecting Variables in Poisson Regression Models

Cites Work

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• Variable selection for the growth curve model
• Cross-validation and multinomial prediction
• Further analysis of the data by Akaike's information criterion and the finite corrections
• Modified AIC and Cp in multivariate linear regression
• Model Selection for Multivariate Regression in Small Samples
• A generalized multivariate analysis of variance model useful especially for growth curve problems
• Some Comments on C P

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