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Abstract Details
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Activity Number:
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86
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Type:
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Contributed
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Date/Time:
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Sunday, July 31, 2011 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Statistics and the Environment
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| Abstract - #302361 |
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Title:
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Analysis of Binary Data via Centered Spatial-Temporal Autologistic Regression Model
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Author(s):
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Zilong Wang*+
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Companies:
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University of Kentucky
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Address:
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Department of Statistics, Lexington, KY, 40506,
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Keywords:
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Autologistic model ;
Gibbs sampler ;
Spatial-temporal process
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Abstract:
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A centered spatial-temporal autologistic model is developed for analyzing spatial-temporal binary data observed on a lattice. We propose expectation-maximization pseudolikelihood (EMPL), expectation-maximization Monte Carlo maximum-likelihood (EMML) and Bayesian inference to obtain the estimates of model parameters and further compare the efficiency of the three approaches for various sizes of lattices and length of time periods. Regarding prediction, we use Monte Carlo to obtain predictive distributions at future time points and compare the performance of the model with the traditional 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.
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Authors who are presenting talks have a * after their name.
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