Optimal Estimation in a Linear Regression Model using Incomplete Prior Information

Helge Toutenburg (), Shalabh () and Christian Heumann ()
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Helge Toutenburg: Universität München, Institut für Statistik
Shalabh: Indian Institute of Technology Kanpur, Department of Mathematics & Statistics
Christian Heumann: Universität München, Institut für Statistik

A chapter in Statistical Inference, Econometric Analysis and Matrix Algebra, 2009, pp 185-199 from Springer

Abstract: Abstract For the estimation of regression coefficients in a linear model when incomplete prior information is available, the optimal estimators in the classes of linear heterogeneous and linear homogeneous estimators are considered. As they involve some unknowns, they are operationalized by substituting unbiased estimators for the unknown quantities. The properties of resulting feasible estimators are analyzed and the effect of operationalization is studied. A comparison of the heterogeneous and homogeneous estimation techniques is also presented.

Keywords: Linear Regression Model; Risk Function; Variance Covariance Matrix; Optimal Estimator; Multivariate Normal Distribution (search for similar items in EconPapers)
Date: 2009
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2121-5_13

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DOI: 10.1007/978-3-7908-2121-5_13

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