Minimax multivariate empirical Bayes estimators under multicollinearity

DOI10.1016/j.jmva.2004.02.018zbMath1066.62014OpenAlexW2055208440MaRDI QIDQ1776877

Tatsuya Kubokawa, Muni S. Srivastava

Publication date: 12 May 2005

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2004.02.018

zbMATH Keywords

Multivariate normal distributionEmpirical Bayes estimatorRidge regression estimatorMulticollinearityMultivariate linear regression model

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05) Minimax procedures in statistical decision theory (62C20) Empirical decision procedures; empirical Bayes procedures (62C12)

Uses Software

• SenSrivastava

Cites Work

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• On estimation of a matrix of normal means with unknown covariance matrix
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• Adaptive multivariate ridge regression
• Estimation of the mean of a multivariate normal distribution
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• Minimax Adaptive Generalized Ridge Regression Estimators
• Predicting Multivariate Response in Linear Regression Model
• UNBIASED ESTIMATOR OF RISK FOR AN ORTHOGONALLY INVARIANT ESTIMATOR OF A COVARIANCE MATRIX
• Ridge Regression: Biased Estimation for Nonorthogonal Problems
• Robust improvement in estimation of a mean matrix in an elliptically contoured distribution

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