Improved minimax estimation of powers of the variance of a multivariate normal distribution under the entropy loss function

DOI10.1016/0167-7152(94)00172-5zbMath0837.62010OpenAlexW2014979387MaRDI QIDQ1903158

Chiahua Ling, Nabendu Pal

Publication date: 20 May 1996

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

Full work available at URL: https://doi.org/10.1016/0167-7152(94)00172-5

zbMATH Keywords

entropy lossquadratic lossmultivariate normal distributionrisk functionbest affine equivariant estimatorsuniform dominationpowers of varianceStrawderman type minimax estimators

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Minimax procedures in statistical decision theory (62C20) Admissibility in statistical decision theory (62C15)

Related Items (9)

Strawderman-type estimators for a scale parameter with application to the exponential distribution ⋮ Improved estimation of the covariance matrix and the generalized variance of a multivariate normal distribution: some unifying results ⋮ Point and interval estimation of powers of scale parameters for two normal populations with a common mean ⋮ Improved estimation of the scale parameter, the hazard rate parameter and the ratio of the scale parameters in exponential distributions: an integrated approach ⋮ Stein-type improved estimation of standard error under asymmetric LINEX loss function ⋮ On the estimation of a normal precision and a normal variance ratio ⋮ A new class of minimax generalized Bayes estimators of a normal variance ⋮ Estimation of a normal variance -- a critical review ⋮ Improved estimation of the generalized precision under the squared loss

Cites Work

• Unnamed Item
• Risk behavior of variance estimators in multivariate normal distribution
• A sequence of improvements over the James-Stein estimator
• Minimax estimation of powers of the variance of a normal population under squared error loss
• Improving on equivariant estimators
• Inadmissibility of the usual estimator for the variance of a normal distribution with unknown mean
• Inadmissibility of the Usual Estimators of Scale parameters in Problems with Unknown Location and Scale Parameters

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