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On Minimax Estimation in Linear Regression Models with Ellipsoidal Constraints

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  • Norbert Christopeit
  • Kurt Helmes
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    Abstract

    We consider the simultaneous linear minimax estimation problem in linear models with ellipsoidal constraints imposed on the unknown parameter. Using convex analysis we derive necessary and sufficient optimality conditions for a matrix to define the best linear minimax estimator. For certain regions of the set of characteristics of the linear models and the constraints we exploit these optimality conditions and get explicit formulae for best linear minimax estimators.

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    Bibliographic Info

    Paper provided by University of Bonn, Germany in its series Discussion Paper Serie B with number 205.

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    Length: 17 pages
    Date of creation: Oct 1991
    Date of revision:
    Handle: RePEc:bon:bonsfb:205

    Contact details of provider:
    Postal: Bonn Graduate School of Economics, University of Bonn, Adenauerallee 24 - 26, 53113 Bonn, Germany
    Fax: +49 228 73 6884
    Web page: http://www.bgse.uni-bonn.de

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    Cited by:
    1. Christopeit, N. & V. L. Girko, 1995. "Minimax Estimator for linear models with nonrandom disturbances," Discussion Paper Serie B 359, University of Bonn, Germany.

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