Selection of Variables in Multivariate Regression Models for Large Dimensions

DOI10.1080/03610926.2011.624242zbMath1271.62117OpenAlexW2113479332MaRDI QIDQ2920051

Muni S. Srivastava, Tatsuya Kubokawa

Publication date: 23 October 2012

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2011.624242

zbMATH Keywords

Akaike information criterionMallows' \(C_p\)

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) Asymptotic distribution theory in statistics (62E20)

Related Items (7)

High-dimensional consistency of rank estimation criteria in multivariate linear model ⋮ Identification of outlying and influential data with principal components regression estimation in binary logistic regression ⋮ Lasso penalized model selection criteria for high-dimensional multivariate linear regression analysis ⋮ 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 ⋮ Optimal model averaging for multivariate regression models ⋮ Consistency of test-based method for selection of variables in high-dimensional two-group discriminant analysis

Cites Work

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• Limit of the smallest eigenvalue of a large dimensional sample covariance matrix
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• Information criteria and statistical modeling.
• Further analysis of the data by Akaike's information criterion and the finite corrections
• Some Comments on C P
• A new look at the statistical model identification

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