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Activity Number: 97
Type: Topic Contributed
Date/Time: Monday, July 30, 2007 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #309012
Title: Weak Consistency of General Bayesian Kernel Mixture in Density Estimation
Author(s): Yuefeng Wu*+ and Subhashis Ghoshal
Companies: North Carolina State University and North Carolina State University
Address: 1251 University Ct Apt 304, Raleigh, NC, 27606,
Keywords: Posterior consistency ; mixture ; Bayesian ; nonparametric ; density estimation
Abstract:

The Bayesian kernel mixture models for estimating probability densities are used in different model structures and with many different kernels. Posterior consistency is not studied for all these models. In this paper, we unify these models in a general form and give conditions under which the posterior weak consistency can be achieved in such general form. It is an improvement over the existing results since it covers a wide variety of kernels. As examples, we give the conditions for posterior weak consistency in the models using normal, skewed normal, t, Laplace, Logistic, Beta, Triangular, log-normal, Gamma and Weibull densities as kernels.

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Revised September, 2007