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Modelling the distribution of plant species using the autologistic regression model
 Hulin Wu,
 F Red W. Huffer
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For modeling the distribution of plant species in terms of climate covariates, we consider an autologistic regression model for spatial binary data on a regularly spaced lattice. This model belongs to the class of autologistic models introduced by Besag (1974). Three estimation methods, the coding method, maximum pseudolikelihood method and Markov chain Monte Carlo method are studied and comparedvia simulation and real data examples. As examples, we use the proposed methodology to model the distributions of two plant species in the state of Florida.
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 Title
 Modelling the distribution of plant species using the autologistic regression model
 Journal

Environmental and Ecological Statistics
Volume 4, Issue 1 , pp 3148
 Cover Date
 19970301
 DOI
 10.1023/A:1018553807765
 Print ISSN
 13528505
 Online ISSN
 15733009
 Publisher
 Kluwer Academic Publishers
 Additional Links
 Topics
 Keywords

 binary data
 coding method
 ecological data
 environmental statistics
 Markov chain Monte Carlo
 plant species
 pseudolikelihood
 spatial data
 Authors

 Hulin Wu ^{(1)}
 F Red W. Huffer ^{(2)}
 Author Affiliations

 1. Frontier Science & Technology Research Foundation, Inc., 303 Bolyston Street, Brookline, MA, 02146, USA
 2. Department of Statistics, The Florida State University, Tallahassee, FL, 32306, USA