Optimization of ridge parameters in multivariate generalized ridge regression by plug-in methods

zbMath1257.62081MaRDI QIDQ1940034

Kenichi Satoh, Isamu Nagai, Hirokazu Yanagihara

Publication date: 5 March 2013

Published in: Hiroshima Mathematical Journal (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.hmj/1355238371

zbMATH Keywords

model selectionplug-in methodmultivariate linear regression modelMallows' \(C_p\) statisticnon-iterative estimation

Mathematics Subject Classification ID

Related Items (8)

A fast and consistent variable selection method for high-dimensional multivariate linear regression with a large number of explanatory variables ⋮ Asymptotic Optimality of Cp-Type Criteria in High-Dimensional Multivariate Linear Regression Models ⋮ Unnamed Item ⋮ Ridge parameters optimization based on minimizing model selection criterion in multivariate generalized ridge regression ⋮ Generalized ridge estimator and model selection criteria in multivariate linear regression ⋮ Equivalence between adaptive Lasso and generalized ridge estimators in linear regression with orthogonal explanatory variables after optimizing regularization parameters ⋮ Consistent variable selection criteria in multivariate linear regression even when dimension exceeds sample size ⋮ A fast algorithm for optimizing ridge parameters in a generalized ridge regression by minimizing a model selection criterion

Cites Work

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• An Explicit Solution for Generalized Ridge Regression
• Modified AIC and Cp in multivariate linear regression
• Generalized ridge regression and a generalization of theC_Pstatistic
• Applied Multivariate Analysis
• Ridge Regression: Biased Estimation for Nonorthogonal Problems
• The optimal set of principal component restrictions on a least-squares regression
• Some Comments on C P

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