Conditions for Consistency of a Log-Likelihood-Based Information Criterion in Normal Multivariate Linear Regression Models under the Violation of the Normality Assumption

DOI10.14490/JJSS.45.21zbMath1341.62230OpenAlexW2114326118MaRDI QIDQ2811388

Hirokazu Yanagihara

Publication date: 10 June 2016

Published in: JOURNAL OF THE JAPAN STATISTICAL SOCIETY (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.14490/jjss.45.21

zbMATH Keywords

variable selectionAICBICmultivariate linear regression modelnonnormalityselection probabilityassumption of normalitybias-corrected AICconsistent AIChigh-dimensional asymptotic frameworkHQClarge-sample asymptotic framework

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Linear regression; mixed models (62J05)

Related Items (6)

High-dimensional asymptotic behavior of the difference between the log-determinants of two Wishart matrices ⋮ Asymptotics of AIC, BIC and \(C_p\) model selection rules in high-dimensional regression ⋮ Asymptotic Optimality of Cp-Type Criteria in High-Dimensional Multivariate Linear Regression Models ⋮ Unnamed Item ⋮ Consistent variable selection criteria in multivariate linear regression even when dimension exceeds sample size ⋮ A consistent variable selection method in high-dimensional canonical discriminant analysis

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