JSM 2005 Online Program

Abstract #304840

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:	350
Type:	Topic Contributed
Date/Time:	Wednesday, August 10, 2005 : 8:30 AM to 10:20 AM
Sponsor:	Section on Bayesian Statistical Science
Abstract - #304840
Title:	Spatial Stochastic Volatility
Author(s):	Jun Yan*+
Companies:	The University of Iowa
Address:	241 Schaeffer Hall, Iowa City, IA, 52242, United States
Keywords:	conditional autoregressive model ; MCMC ; nomal mixture ; spatial statistics ; stochastic volatility
Abstract:	A widely used spatial areal model (Besag, York, and Mollie 1991) assumes that the error is composed of two terms: one capturing the regional clustering and the other representing regional heterogeneity. The regional heterogeneity terms are assumed to be independent and identically distributed normal variables, which may fail to deliver the spatial heteroskedasticity arising in many real datasets and, therefore, fail to provide good prediction intervals. This paper introduces a new class of spatial processes with spatial stochastic volatility to model the regional heterogeneity. These are mean zero, conditionally independent processes given a latent spatial process of the variances. The logarithm of the latent variance process can be modeled using a conditional or intrinsic conditional auto regression. The spatial stochastic volatility (SSV) model relaxes the traditional homeskedasticity assumption for spatial heterogeneity and brings great flexibility to the popular spatial statistical models.

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