Bayes estimates in multivariate semiparametric linear models - Olaf Bunke (Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes, 2002)
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.
urn:nbn:de:kobv:11-10049163
Keywords: 330; Dirichlet prior; Multivariate linear model; location-scale model; twosample model