Minimax estimation of a normal mean vector when the covariance matrix is unknown

DOI10.1214/aos/1176344733zbMath0418.62004OpenAlexW2065629876MaRDI QIDQ1131804

Leon Jay Gleser

Publication date: 1979

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/aos/1176344733

zbMATH Keywords

unknown covariance matrixWishart distributionminimax estimationquadratic lossnormal mean vector

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Point estimation (62F10) Minimax procedures in statistical decision theory (62C20)

Related Items (8)

Minimax estimation of independent normal means under a quadratic loss function with unknown weights ⋮ Shrinkage estimation of contemporaneous outliers in concurrent time serie ⋮ Shrinkage estimation in time series using a bootstrapped covariance estimate ⋮ Bayes minimax estimators of a multivariate normal mean, with application to generalized ridge regression ⋮ Minimax estimators in the MANOVA model for arbitrary quadratic loss and unknown covariance matrix ⋮ Improved multivariate normal mean estimation with unknown covariance when \(p\) is greater than \(n\) ⋮ Robust shrinkage estimation for elliptically symmetric distributions with unknown covariance matrix ⋮ Improved estimators for the GMANOVA problem with application to Monte Carlo simulation

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