Computational Issues in Spatial-Temporal Autologistic Regression Modeling

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.