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Bayes estimates in multivariate semiparametric linear models

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  • Bunke, Olaf

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.

Suggested Citation

  • Bunke, Olaf, 2002. "Bayes estimates in multivariate semiparametric linear models," SFB 373 Discussion Papers 2002,58, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    • Handle: RePEc:zbw:sfb373:200258
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    References listed on IDEAS

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    1. SIMAR, Leopold, 1984. "A survey of Bayesian approaches to nonparametric statistics," LIDAM Reprints CORE 568, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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    1. Bunke, Olaf & Johannes, Jan, 2003. "Selfinformative Limits of Bayes Estimates and Generalized Maximum Likelihood," SFB 373 Discussion Papers 2003,5, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

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