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Modeling Poisson variables with positive spatial dependence

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  • Kaiser, Mark S.
  • Cressie, Noel

Abstract

The Poisson auto-model is a natural vehicle for modeling data that consist of small counts and may exhibit dependence, frequently spatial dependence. Unfortunately, it is not possible to model positive dependence with a regular Poisson auto-model. We develop a model that allows positive dependencies in multivariate count data by specifying conditional distributions as Winsorized Poisson probability mass functions. This model may be used to incorporate either positive or negative dependencies among the variables.

Suggested Citation

  • Kaiser, Mark S. & Cressie, Noel, 1997. "Modeling Poisson variables with positive spatial dependence," Statistics & Probability Letters, Elsevier, vol. 35(4), pages 423-432, November.
  • Handle: RePEc:eee:stapro:v:35:y:1997:i:4:p:423-432
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    References listed on IDEAS

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    1. Fakhre-Zakeri, Issa & Farshidi, Jamshid, 1993. "A central limit theorem with random indices for stationary linear processes," Statistics & Probability Letters, Elsevier, vol. 17(2), pages 91-95, May.
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    Cited by:

    1. David Mushinski & Stephan Weiler & Benjamin Widner, 2014. "The impact of retail establishments in hinterlands on the export role of retail establishments in rural places," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 52(2), pages 469-487, March.
    2. Liesenfeld, Roman & Richard, Jean-François & Vogler, Jan, 2013. "Analysis of discrete dependent variable models with spatial correlation," Economics Working Papers 2013-01, Christian-Albrechts-University of Kiel, Department of Economics.
    3. !ngel AlanÛn & Rafael Myro, "undated". "Does neighboring "industrial atmosphere" matter in industrial location?. Empirical evidence from Spanish municipalities," Studies on the Spanish Economy 199, FEDEA.
    4. Griffith, Daniel A., 2002. "A spatial filtering specification for the auto-Poisson model," Statistics & Probability Letters, Elsevier, vol. 58(3), pages 245-251, July.
    5. 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.
    6. 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.
    7. Angel Alañón Pardo & Josep Maria Arauzo Carod, 2009. "Accessibility and Industrial Location: evidence from Spain," Documentos de trabajo de la Facultad de Ciencias Económicas y Empresariales 09-01, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales.

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