Bayes estimates in multivariate semiparametric linear 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.
|Date of creation:||2002|
|Date of revision:|
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