Shrinkage minimax estimation and positive-part rule for a mean matrix in an elliptically contoured distribution

DOI10.1016/j.spl.2009.10.009zbMath1180.62084OpenAlexW2038545192MaRDI QIDQ844874

Hisayuki Tsukuma

Publication date: 5 February 2010

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2009.10.009

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)

Related Items (4)

Ridge-type linear shrinkage estimation of the mean matrix of a high-dimensional normal distribution ⋮ The distribution of the Liu-type estimator of the biasing parameter in elliptically contoured models ⋮ A unified approach to estimating a normal mean matrix in high and low dimensions ⋮ Weighted shrinkage estimators of normal mean matrices and dominance properties

Cites Work

• On estimation of a matrix of normal means with unknown covariance matrix
• On singular Wishart and singular multivariate beta distributions
• A Family of Minimax Estimators of the Mean of a Multivariate Normal Distribution
• Empirical Bayes on vector observations: An extension of Stein's method
• Robust improvement in estimation of a mean matrix in an elliptically contoured distribution
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