Minimax estimator of regression coefficient in normal distribution under balanced loss function

DOI10.1016/j.laa.2011.08.013zbMath1232.62089OpenAlexW2038548028MaRDI QIDQ665945

Ping Peng, Guikai Hu, Qing-Guo Li

Publication date: 7 March 2012

Published in: Linear Algebra and its Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.laa.2011.08.013

zbMATH Keywords

normal distributions

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05) Minimax procedures in statistical decision theory (62C20)

Related Items (2)

Bayes minimax estimation of the multivariate normal mean vector under balanced loss function ⋮ The admissible minimax estimator in Gauss–Markov model under a balanced loss function

Cites Work

• The linear minimax estimator of stochastic regression coefficients and parameters under quadrat\-ic loss function
• Admissibility for linear estimators of regression coefficient in a general Gauss-Markoff model under balanced loss function
• Families of minimax estimators of the mean of a multivariate normal distribution
• Inadmissibility of the Stein-rule estimator under the balanced loss function
• \(\Phi\) admissibility for linear estimators on regression coefficients in a general multivariate linear model under balanced loss function
• On estimation with weighted balanced-type loss function
• A family of admissible minimax estimators of the mean of a multivariate normal distribution
• The exact risks of some pre-test and stein-type regression estimators umder balanced loss
• The Efficiency of Shrinkage Estimators with Respect to Zellner's Balanced Loss Function
• The exact risk performance of a pre-test estimator in a heteroskedastic linear regression model under the balanced loss function
• Weighted balanced loss function and estimation of the mean time to failure
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