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Bayes prediction density and regression estimation — A semiparametric approach

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Abstract

This paper is concerned with the Bayes estimation of an arbitrary multivariate density,f(x), x ∃ R k. Such anf(x) may be represented as a mixture of a given parametric family of densities {h (x¦θ)} with support inR k, whereθ (inR d) is chosen according to a mixing distributionG. We consider the semiparametric Bayes approach in whichG, in turn, is chosen according to a Dirichlet process prior with given parameterα. We then specialize these results whenf is expressed as a mixture of multivariate normal densitiesΦ (x¦Μ, λ) whereΜ is the mean vector and λ is the precision matrix. The results are finally applied to estimating a regression parameter.

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Authors and Affiliations

  1. University of North Carolina, Charlotte, USA

    R. C. Tiwari

  2. University of California, Santa Barbara, USA

    S. R. Jammalamadaka

  3. University of Missouri, Columbia, USA

    S. Chib

Authors
  1. R. C. Tiwari
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  2. S. R. Jammalamadaka
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  3. S. Chib
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Tiwari, R.C., Jammalamadaka, S.R. & Chib, S. Bayes prediction density and regression estimation — A semiparametric approach. Empirical Economics 13, 209–222 (1988). https://doi.org/10.1007/BF01972449

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  • DOI: https://doi.org/10.1007/BF01972449

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