Analysis of binary data via a centered spatial-temporal autologistic regression model

A centered spatial-temporal autologistic model is developed for analyzing spatial-temporal binary data observed on a lattice over time. We propose expectation-maximization pseudolikelihood and Monte Carlo expectation-maximization likelihood as well as consider Bayesian inference to obtain the estimates of model parameters. Further, we compare the statistical efficiency of the three approaches for various sizes of sampling lattices and numbers of sampling time points. Regarding prediction, we use Monte Carlo to obtain predictive distributions at future time points and compare the performance of the model with the uncentered spatial-temporal autologistic regression model. The methodology is demonstrated via simulation studies and a real data example concerning southern pine beetle outbreak in North Carolina.