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Confidence Sets for the Coefficients Vector of a Linear Regression Model with Nonspherical Disturbances
Published online by Cambridge University Press: 11 February 2009
Abstract
In this present paper, considering a linear regression model with nonspherical disturbances, improved confidence sets for the regression coefficients vector are developed using the Stein rule estimators. We derive the large-sample approximations for the coverage probabilities and the expected volumes of the confidence sets based on the feasible generalized least-squares estimator and the Stein rule estimator and discuss their ranking.
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- Copyright © Cambridge University Press 1997
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