A spatial filtering specification for the auto-Poisson model
AbstractThe auto-Poisson model describes georeferenced data consisting of counts exhibiting spatial dependence. Its conventional specification is plagued by being restricted to only situations involving negative spatial autocorrelation, and an intractable normalizing constant. Work summarized here accounts for spatial autocorrelation in the mean response specification by incorporating latent map pattern components. Results are reported for seven empirical datasets available in the literature.
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Bibliographic InfoArticle provided by Elsevier in its journal Statistics & Probability Letters.
Volume (Year): 58 (2002)
Issue (Month): 3 (July)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Kaiser, Mark S. & Cressie, Noel, 1997. "Modeling Poisson variables with positive spatial dependence," Statistics & Probability Letters, Elsevier, vol. 35(4), pages 423-432, November.
- Dayton M. Lambert & Jason P. Brown & Raymond J.G.M. Florax, 2010.
"A Two-Step Estimator For A Spatial Lag Model Of Counts: Theory, Small Sample Performance And An Application,"
10-5, Purdue University, College of Agriculture, Department of Agricultural Economics.
- Lambert, Dayton M. & Brown, Jason P. & Florax, Raymond J.G.M., 2010. "A two-step estimator for a spatial lag model of counts: Theory, small sample performance and an application," Regional Science and Urban Economics, Elsevier, vol. 40(4), pages 241-252, July.
- Daniel Griffith, 2009. "Modeling spatial autocorrelation in spatial interaction data: empirical evidence from 2002 Germany journey-to-work flows," Journal of Geographical Systems, Springer, vol. 11(2), pages 117-140, June.
- Haining, Robert & Law, Jane & Griffith, Daniel, 2009. "Modelling small area counts in the presence of overdispersion and spatial autocorrelation," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2923-2937, June.
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