期刊名称:Sankhya. Series A, mathematical statistics and probability
印刷版ISSN:0976-836X
电子版ISSN:0976-8378
出版年度:2006
卷号:68
期号:01
出版社:Indian Statistical Institute
摘要:We provide sufficient conditions under which a Dirichlet location-scale mixture of normal prior achieves weak and strong posterior consistency at a true density. Our conditions involve both the prior and the true density from which observations are obtained. We consider it to be a significant improvement over the existing results since our conditions cover the case of fat tailed densities like the Cauchy, with a standard choice for the base measure of the Dirichlet process. This provides a wider choice for using these popular mixture priors for nonparametric density estimation and semiparametric regression problems.
关键词:Posterior consistency, Dirichlet process, location-scale mixtures, density estimation, regression.