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Fuzzy prior information and minimax estimation in the linear regression model

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Abstract

We consider the linear regression modely=Xβ+u with prior information on the unknown parameter vector β. The additional information on β is given by a fuzzy set. Using the mean squared error criterion we derive linear estimators that optimally combine the data with the fuzzy prior information. Our approach generalizes the classical minimax procedure firstly proposed by Kuks and Olman.

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Authors and Affiliations

  1. Institut für Statistik und Ökonometrie, Universität Hamburg, Von-Melle-Park 5, D-20146, Hamburg, Germany

    Bernhard F. Arnold & Peter Stahlecker

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  1. Bernhard F. Arnold
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  2. Peter Stahlecker
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Arnold, B.F., Stahlecker, P. Fuzzy prior information and minimax estimation in the linear regression model. Statistical Papers 38, 377–391 (1997). https://doi.org/10.1007/BF02925995

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  • DOI: https://doi.org/10.1007/BF02925995

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