Estimation of a normal covariance matrix with incomplete data under Stein's loss

DOI10.1006/jmva.1995.1016zbMath0816.62043OpenAlexW2063991982MaRDI QIDQ1347085

Yoshihiko Konno

Publication date: 3 July 1995

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

Full work available at URL: https://doi.org/10.1006/jmva.1995.1016

zbMATH Keywords

incomplete datacovariance matrixmaximum likelihood estimatorminimax estimatorsGMANOVA modelunbiased estimateinvariant estimatorStein's loss function

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Foundations and philosophical topics in statistics (62A01)

Related Items (8)

The Stein phenomenon for monotone incomplete multivariate normal data ⋮ The Bayes rule of the parameter in (0,1) under Zhang’s loss function with an application to the beta-binomial model ⋮ Further results on estimation of covariance matrix ⋮ A new estimator of covariance matrix via partial Iwasawa coordinates ⋮ A new estimator of covariance matrix ⋮ Estimation of multivariate normal covariance and precision matrices in a star-shape model with missing data ⋮ Inadmissibility of the maximum likelihood estimator of normal covariance matrices with the lattice conditional independence ⋮ Minimax estimation of a normal covariance matrix with the partial Iwasawa decomposition

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