Bayes estimates in multivariate semiparametric linear models
AbstractBayes 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. --
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes in its series SFB 373 Discussion Papers with number 2002,58.
Date of creation: 2002
Date of revision:
Dirichlet prior; Multivariate linear model; location-scale model; twosample model;
You can help add them by filling out this form.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (ZBW - German National Library of Economics).
If references are entirely missing, you can add them using this form.