Estimation of the variance in a normal population after the one-sided pre-test for the mean

DOI10.1080/03610929108830492zbMath0732.62024OpenAlexW2083321240MaRDI QIDQ3358051

Kazuhiro Ohtani

Publication date: 1991

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610929108830492

zbMATH Keywords

estimation of variancesmall sample propertiesStein estimatorpre-test estimatorone-sided alternativenormal populationoptimal critical value

Mathematics Subject Classification ID

Point estimation (62F10)

Related Items (6)

Risk behavior of a pre-test estimator for normal variance with the Stein- type estimator ⋮ The exact distribution and density functions of the stein-type estimator for normal variance ⋮ Estimating the error variance after a pre-test for an inequality restriction on the coefficients ⋮ A minimum discrimination information estimator of preliminary conjectured normal variance ⋮ On the sampling performance of an inequality pre-test estimator of the regression error variance under LINEX loss ⋮ Optimal critical values of pre-tests when estimating the regression error variance: Analytical findings under a general loss structure

Cites Work

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• Optimal levels of significance of a pre-test in estimating the disturbance variance after the pre-test for a linear hypothesis on coefficients in a linear regression
• Estimating the error variance in regression after a preliminary test of restrictions on the coefficients
• Improved estimation of a covariance matrix under quadratic loss
• Improved estimation of the disturbance variance in a linear regression model
• Estimating a bounded normal mean
• 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
• Improved estimation of variance components in mixed models
• Inadmissibility of the Usual Estimators of Scale parameters in Problems with Unknown Location and Scale Parameters
• Mean Square Efficiency of Estimators of Variance Components

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