Autologistic models for binary data on a lattice

Environmetrics

Volumn 22, Issue 7, 2011, Pages 857-871

  Hughes, John ^a   Haran, Murali ^a   Caragea, Petruţa C ^b  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

^b Department of Statistics Center for Statistics and Applications in Forensic Evidence Iowa State University 2438 Osborn Dr Ames   (United States)

Author keywords

Bayesian; Markov random field; Maximum likelihood; Parallel computing; Perfect sampling; Pseudolikelihood; Spatial

Indexed keywords

ALGORITHM; BAYESIAN ANALYSIS; MARKOV CHAIN; MAXIMUM LIKELIHOOD ANALYSIS; OPTIMIZATION; PARALLEL COMPUTING; PARAMETERIZATION; SAMPLING; SPATIAL ANALYSIS; SPATIAL DATA; TWO-DIMENSIONAL MODELING;

ANIMALIA;

EID: 80055093312     PISSN: 11804009     EISSN: 1099095X     Source Type: Journal    
DOI: 10.1002/env.1102     Document Type: Article

References (27)

1. Augustin NH, Mugglestone MA, Buckland ST.1996. An autologistic model for the spatial distribution of wildlife. Journal of Applied Ecology33:339-347.
2. Banerjee S, Carlin B, Gelfand A.2004. Hierarchical Modeling and Analysis for Spatial Data. Chapman & Hall Ltd. Boca Raton, Florida.
3. Besag J.1974. Spatial interaction and the statistical analysis of lattice systems (with discussion). Journal of the Royal Statistical Society, Series B: Methodological36:192-236.
4. Besag J.1975. Statistical analysis of non-lattice data. The Statisti-cian: Journal of the Institute of Statisticians24:179-196.
5. Besag JE.1972. Nearest-neighbour systems and the auto-logistic model for binary data. Journal of the Royal Statistical Society, Series B: Methodological34:75-83.
6. Brook D.1964. On the distinction between the conditional probability and the joint probability approaches in the specication of nearest-neighbour systems. Biometrika51:481-483.
7. Caragea P, Kaiser M.2009. Autologistic models with interpretable parameters. Journal of Agricultural, Biological, and Envi-ronmental Statistics14:281-300.
8. Cressie NA.1993. Statistics for Spatial Data, 2nd edition. John Wiley & Sons: New York.
9. De Oliveira V.2000. Bayesian prediction of clipped Gaussian random elds. Computational Statistics & Data Analysis34:299-314.
10. Dormann C.2007. Assessing the validity of autologistic regression. Ecological Modelling207:234-242.
11. Geyer C.1994. On the convergence of Monte Carlo maximum likelihood calculations. Journal of the Royal Statistical Society. Series B (Methodological)56:261-274.
12. Gumpertz ML, Graham JM, Ristaino JB.1997. Autologistic model of spatial pattern of phytophthora epidemic in bell pepper: Effects of soil variables on disease presence. Journal of Agri-cultural, Biological, and Environmental Statistics2:131-156.
13. He F, Zhou J, Zhu H.2003. Autologistic regression model for the distribution of vegetation. Journal of Agricultural, Biological, and Environmental Statistics8:205-222.
14. Hoeting JA, Leecaster M, Bowden D.2000. An improved model for spatially correlated binary responses. Journal of Agricul-tural, Biological, and Environmental Statistics5:102-114.
15. Huffer F, Wu H.1998. Markov chain Monte Carlo for autologistic regression models with application to the distribution of plant species. Biometrics54:509-524.
16. Ihaka R, Gentleman R.1996.R: A language for data analysis and graphics. Journal of Computational and Graphical Statistics5:299-314.
17. Jones PW, Donovan TM.1996. Hermit thrush (Catharus guttatus). InThe Birds of North America Online, Poole A (ed.), Cornell Lab of Ornithology: Ithaca.
18. Kaiser M, Cressie N.2000. The construction of multivariate distributions from markov random elds. Journal of Multivariate Analysis73:199-220.
19. Koutsias N.2003. An autologistic regression model for increasing the accuracy of burned surface mapping using Landsat Thematic Mapper data. International Journal of Remote Sensing24:2199-2204.
20. Møller J.1999. Perfect simulation of conditionally specied models. Journal of the Royal Statistical Society, Series B, Methodological61:251-264.
21. Møller J, Pettitt A, Berthelsen K, Reeves R.2006. An effcient Markov chain Monte Carlo method for distributions with intractable normalising constants. Biometrika93:451-458.
22. Moon S, Russell GJ.2008. Predicting product purchase from inferred customer similarity: An autologistic model approach. Man-agement Science54:71-82.
23. Murray I, Ghahramani Z, MacKay D.2006.MCMC for doublyintractable distributions. Proceedings of the 22nd Annual Conference on Uncertainty in Articial Intelligence (UAI), 359-366.
24. Propp JG, Wilson DB.1996. Exact sampling with coupled Markov chains and applications to statistical mechanics. Random Structures and Algorithms9:223-252.
25. Sanderson RA, Eyre MD, Rushton SP.2005. Distribution of selected macroinvertebrates in a mosaic of temporary and permanent freshwater ponds as explained by autologistic models. Ecography28:355-362.
26. Tierney L, Rossini A, Li N.2009. Snow: A parallel computing framework for the r system. International Journal of Parallel Programming37:78-90.
27. Zheng Y, Zhu J.2008. Markov Chain Monte Carlo for a spatial-temporal autologistic regression model. Journal of Computational and Graphical Statistics17:123-137.