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Optimal Estimation in a Linear Regression Model using Incomplete Prior Information

In: Statistical Inference, Econometric Analysis and Matrix Algebra

Author

Listed:
  • Helge Toutenburg

    (Universität München, Institut für Statistik)

  • Shalabh

    (Indian Institute of Technology Kanpur, Department of Mathematics & Statistics)

  • Christian Heumann

    (Universität München, Institut für Statistik)

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.

Suggested Citation

  • Helge Toutenburg & Shalabh & Christian Heumann, 2009. "Optimal Estimation in a Linear Regression Model using Incomplete Prior Information," Springer Books, in: Bernhard Schipp & Walter Kräer (ed.), Statistical Inference, Econometric Analysis and Matrix Algebra, pages 185-199, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2121-5_13
    DOI: 10.1007/978-3-7908-2121-5_13
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