The exact distribution and density functions of a pre-test estimator of the error variance in a linear regression model with proxy variables

DOI10.1007/BF02925404zbMath0887.62075OpenAlexW1988508571MaRDI QIDQ1383242

Kazuhiro Ohtani, Hiroko Kurumai

Publication date: 2 April 1998

Published in: Statistical Papers (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02925404

zbMATH Keywords

critical valuecoverage probabilityinterval estimationpre-test estimator

Mathematics Subject Classification ID

Parametric tolerance and confidence regions (62F25) Linear regression; mixed models (62J05) Point estimation (62F10) Exact distribution theory in statistics (62E15)

Related Items (1)

The exact density and distribution functions of the inequality constrained and pre-test estimators

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
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• Pre-testing for linear restrictions in a regression model with spherically symmetric disturbances
• The exact distribution of a least squares regression coefficient estimator after a preliminary \(t\)-test
• MSE dominance of least squares with errors-of-observation
• Improved invariant set estimation for general scale families
• The density function and the MSE dominance of the pre-test estimator in a heteroscedastic linear regression model with omitted variables
• Testing linear restrictions on coefficients in a linear regression model with proxy variables and spherically symmetric disturbances
• Inadmissibility of the usual estimator for the variance of a normal distribution with unknown mean
• The Use of Proxy Variables when One or Two Independent Variables are Measured with Error
• Preliminary-test estimation of the regression scale parameter when the loss function is asymmetric
• The exact distribution and density functions of the stein-type estimator for normal variance
• Relative Asymptotic Bias from Errors of Omission and Measurement
• A Note on the Use of Proxy Variables

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