Autologistic regression analysis of spatial-temporal binary data via Monte Carlo maximum likelihood

  • Jun Zhu
  • Yanbing Zheng
  • Allan L. Carroll
  • Brian H. Aukema


This article considers logistic regression analysis of binary data that are measured on a spatial lattice and repeatedly over discrete time points. We propose a spatial-temporal autologistic regression model and draw statistical inference via maximum likelihood. Due to an unknown normalizing constant in the likelihood function, we use Monte Carlo to obtain maximum likelihood estimates of the model parameters and predictive distributions at future time points. We also use path sampling to estimate the unknown normalizing constant and approximate an information criterion for model assessment. The methodology is illustrated by the analysis of a dataset of mountain pine beetle outbreaks in western Canada.

Key Words

AIC Bark beetles Gibbs sampler Mountain pine beetle Path sampling Spatial-temporal process 


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Copyright information

© International Biometric Society 2008

Authors and Affiliations

  • Jun Zhu
    • 1
  • Yanbing Zheng
    • 2
  • Allan L. Carroll
    • 3
  • Brian H. Aukema
    • 4
  1. 1.Department of StatisticsUniversity of Wisconsin-MadisonMadison
  2. 2.Department of StatisticsUniversity of KentuckyLexington
  3. 3.Natural Resources CanadaCanadian Forest Service, Pacific Forest CentreVictoriaCanada
  4. 4.University of Northern British ColumbiaPrince GeorgeCanada

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