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Bayes estimates in multivariate semiparametric linear models

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  • Bunke, Olaf
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    Abstract

    Bayes estimates are derived in multivariate linear models with unknown distribution. The prior distribution is defined using a Dirichlet prior for the unknown error distribution and a ormal-Wishart distribution for the parameters. The posterior distribution for the parameters is determined and is a mixture of normal-Wishart distributions. The posterior mean of the observation distributions is a mixture of generalized Student distributions and of kernel estimates and empirical distributions based on pseudoobservations. Explicit expressions are given in the special cases of location - scale and two-sample models. The calculation of selfinformative limits of Bayes estimates yields standard estimates. --

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    File URL: http://econstor.eu/bitstream/10419/65297/1/727024825.pdf
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    Bibliographic Info

    Paper provided by Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes in its series SFB 373 Discussion Papers with number 2002,58.

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    Date of creation: 2002
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    Handle: RePEc:zbw:sfb373:200258

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    Related research

    Keywords: Dirichlet prior; Multivariate linear model; location-scale model; twosample model;

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