2005-10-10Buch DOI: 10.18452/3504
Bayes Estimates in Multivariate SemiparametricLinear Models
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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