JSM 2005 Online Program

Abstract #303704

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	64
Type:	Contributed
Date/Time:	Sunday, August 7, 2005 : 4:00 PM to 5:50 PM
Sponsor:	Section on Bayesian Statistical Science
Abstract - #303704
Title:	Applications of Levy Processes in Bayesian Nonparametric Modeling
Author(s):	Chong Tu*+ and Merlise Clyde and Robert L. Wolpert
Companies:	Duke University and Duke University and Duke University
Address:	505 Mayfield Circle, Durham, NC, 27705, United States
Keywords:	Levy Process ; Kernel Convolution ; Reversible Jump MCMC ; Bayesian Nonparametrics
Abstract:	We propose a new class of Bayesian nonparametric methods based on Levy process priors for estimation of an unknown function. We induce prior distributions on the unknown function by a kernel convolution of a mixture of Levy processes. Throughout, we use reversible jump Markov chain Monte Carlo methods to compute the posterior distributions of quantities of interest, including point estimates of the function and interval estimates. We demonstrate the methodology with several examples, including nonparametric regression and time series modeling. The method allows us to avoid large matrix inversions and handles both nonstationarity and nonGaussian.

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