JSM 2005 Online Program

Abstract #304062

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 - #304062
Title:	Generalized Spatial Dirichlet Process Model
Author(s):	J. A. Duan*+ and Michele Guindani and Alan E. Gelfand
Companies:	Duke University and Bocconi University and Duke University
Address:	1911 Erwin Rd Apt I, Durham, NC, 27705, United States
Keywords:	Bayesian Noparametrics ; spatial statistics ; Dirichlet process
Abstract:	A spatial Dirichlet process by construction is a random probability measure with its values independently sampled from a stationary Gaussian spatial process and the corresponding weights constructed from a Beta stick-breaking process. It is generalized in this work by allowing spatially varying and correlated weights. These weights are theoretically shown as arising from a generalized stick-breaking process. In practice, using a latent Gaussian spatial process facilitates a convenient weight construction. We demonstrate with simulated data that the generalized Spatial Dirichlet process is a flexible model and has many desired properties. Bayesian posterior inference is implemented using a special Gibbs sampler that we detail. This novel method is extended to a dynamical model version for spatiotemporal data.

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