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Activity Number: 104
Type: Invited
Date/Time: Monday, August 5, 2013 : 8:30 AM to 10:20 AM
Sponsor: ENAR
Abstract - #307169
Title: Estimation and Selection of Autologistic Regression Models for Spatial Binary Data
Author(s): Jun Zhu*+ and Rao Fu and Andrew L Thurman and Michelle M Steen-Adams
Companies: University of Wisconsin-Madison and University of Wisconsin-Madison and University of Wisconsin-Madison and University of New England
Keywords: Markov random field ; model selection ; penalized methods ; spatial statistics
Abstract:

Autologistic regression models are suitable for relating spatial binary responses in ecology to covariates such as environmental factors. Model parameters can be estimated via maximum likelihood or Bayesian methods, although they often involve Monte Carlo simulation and tend to be computationally intensive. For big ecological data, pseudolikelihood estimation is appealing due to its ease of computation, but several challenges remain including model selection and variance estimation. Here we present a penalized pseudolikelihood approach to address some of these issues. A simulation study is conducted to evaluate the performance of this approach, followed by a data example in landscape ecology and environmental history.


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