Logistic regression for spatial Gibbs point processes

Adrian Baddeley, J.F. Coeurjolly, E. Rubak, R.P. Waagepetersen

    Research output: Contribution to journalArticle

    24 Citations (Scopus)

    Abstract

    We propose a computationally efficient technique, based on logistic regression, for fitting Gibbs point process models to spatial point pattern data. The score of the logistic regression is an unbiased estimating function and is closely related to the pseudolikelihood score. Implementation of our technique does not require numerical quadrature, and thus avoids a source of bias inherent in other methods. For stationary processes, we prove that the parameter estimator is strongly consistent and asymptotically normal, and propose a variance estimator. We demonstrate the efficiency and practicability of the method on a real dataset and in a simulation study. © 2014 Biometrika Trust.
    Original languageEnglish
    Pages (from-to)377-392
    Number of pages16
    JournalBiometrika
    Volume101
    Issue number2
    Early online date6 Mar 2014
    DOIs
    Publication statusPublished - 2 Jun 2014

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