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

Abstract Details

Activity Number: 249
Type: Contributed
Date/Time: Monday, August 2, 2010 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract - #306917
Title: Generalized Linear Spatial Random Effects Model
Author(s): Aritra Sengupta*+ and Noel A. Cressie
Companies: The Ohio State University and The Ohio State University
Address: Department of Statistics, Columbus, OH, 43210,
Keywords: non-linear spatial random effects model ; generalized linear model ; hierarchical statistical model ; MCMC
Abstract:

Our research concerns Bayesian inference on non-linear spatial random effects models, with special emphasis in this talk on spatial generalized linear models. Dimension reduction is an important feature of these models. We shall embed them into a hierarchical statistical framework, where the data could take binary, count, or positive values. Inference based on MCMC will be discussed, and the relationship between dimension reduction and computational efficiency will be explored.


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