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" Computational Issues in Spatial-Temporal Autologistic Regression Modeling"

Jun Zhu, Ph.D.

Associate Professor, Department of Statistics

University of Wisconsin, Madison

Monday., September 17, 2007
4:00 p.m.
223 Weber

ABSTRACT

A spatial-temporal autologistic regression model relates a binary response variable to potential covariates, while accounting for both spatial and temporal correlation. This modeling framework is flexible and can be useful for analyzing spatial-temporal binary data. Here I discuss various computational techniques for statistical inference including maximum pseudolikelihood, Monte Carlo maximum likelihood, and Bayesian hierarchical modeling. These approaches are illustrated by a real data example of bark beetle outbreaks.

 

 

 


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