Bayes Prediction Density and Regression Estimation — A Semiparametric Approach
Ram Chandra Tiwari, S. Rao Jammalamadaka, Siddhartha Chib · 1989
This paper is concerned with the Bayes estimation of an arbitrary multivariate density, f ( x ), x ∈ R k . Such an f ( x ) may be represented as a mixture of a given parametric family of densities h( x |θ) with support in R k , where θ (in R d ) is chosen according to a mixing distribution G . We consider the semiparametric Bayes approach in which G , in turn, is chosen according to a Dirichlet process prior with given parameter a. We then specialize these results when f is expressed as a mixture of multivariate normal densities θ( x |μ, Λ) where μ is the mean vector and Λ is the precision matrix. The results are finally applied to estimating a regression parameter. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.