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.
