This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 317
Type: Invited
Date/Time: Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
Sponsor: Statistical and Applied Mathematical Sciences Institute
Abstract - #306065
Title: A Generalized Bayesian Spatial Prediction Method
Author(s): Jim Zidek*+ and Yiping Dou and Nhu Le
Companies: The University of British Columbia and The University of British Columbia and BC Cancer Agency
Address: Department of Statistics, Vancouver, BC, V6T 1Z2, Canada
Keywords: Spatial prediction ; space-time modeling ; empirical orthogonal functions ; dynamic linear models ; Bayesian hierarchial models
Abstract:

This paper presents a generalized Bayesian spatial prediction (GBSP) method for modeling space--time fields as an extension of a Bayesian spatial prediction (BSP) method developed some time ago. We obtain the generalization by integrating the BSP into the dynamic linear modeling (DLM) framework. However the extension preserves the computational advantages that guided the creation of the BSP in the first place, while gaining some of the DLM's advantages for prediction across the entire spatio--temporal region. In particular, the GBSP can handle spatial--temporal fields of high resolution. It also incorporates recently developed Bayesian empirical orthogonal functions for representing complex spatial patterns and covers data generated by multivariate response vectors. The advantages of the approach will be compared to a number of competitors.


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