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    Logistic regression for spatial Gibbs point processes

    Access Status
    Fulltext not available
    Authors
    Baddeley, Adrian
    Coeurjolly, J.
    Rubak, E.
    Waagepetersen, R.
    Date
    2014
    Type
    Journal Article
    
    Metadata
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    Citation
    Baddeley, A. and Coeurjolly, J. and Rubak, E. and Waagepetersen, R. 2014. Logistic regression for spatial Gibbs point processes. Biometrika. 101 (2): pp. 377-392.
    Source Title
    Biometrika
    DOI
    10.1093/biomet/ast060
    ISSN
    0006-3444
    URI
    http://hdl.handle.net/20.500.11937/39331
    Collection
    • Curtin Research Publications
    Abstract

    We propose a computationally efficient technique, based on logistic regression, for fittingGibbs point process models to spatial point pattern data. The score of the logistic regression is anunbiased estimating function and is closely related to the pseudolikelihood score. Implementationof our technique does not require numerical quadrature, and thus avoids a source of bias inherentin other methods. For stationary processes, we prove that the parameter estimator is stronglyconsistent and asymptotically normal, and propose a variance estimator. We demonstrate theefficiency and practicability of the method on a real dataset and in a simulation study.

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