Bayes estimates in multivariate semiparametric linear models

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: SFB 373 Discussion Paper ; No. 2002,58

Classification and Topics

• Classification: Wirtschaft
• Subject: Dirichlet prior ; Multivariate linear model ; location-scale model ; twosample model

Contributors, Location and Time

• Event: Geistige Schöpfung
• (who): Bunke, Olaf
• Event: Veröffentlichung
• (who): Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes
• (where): Berlin
• (when): 2002

Further information

• Handle: http://hdl.handle.net/10419/65297
• URN: urn:nbn:de:kobv:11-10049163
• Last update: 10.03.2025, 11:41 AM CET