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dc.contributor.authorBaddeley, Adrian
dc.contributor.authorCoeurjolly, J.
dc.contributor.authorRubak, E.
dc.contributor.authorWaagepetersen, R.
dc.date.accessioned2017-01-30T14:32:51Z
dc.date.available2017-01-30T14:32:51Z
dc.date.created2015-04-23T03:53:29Z
dc.date.issued2014
dc.identifier.citationBaddeley, A. and Coeurjolly, J. and Rubak, E. and Waagepetersen, R. 2014. Logistic regression for spatial Gibbs point processes. Biometrika. 101 (2): pp. 377-392.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/39331
dc.identifier.doi10.1093/biomet/ast060
dc.description.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.

dc.publisherOxford University Press
dc.subjectGeorgii–Nguyen–Zessin formula
dc.subjectExponential family model
dc.subjectPseudolikelihood
dc.subjectLogistic regression
dc.subjectEstimating function
dc.titleLogistic regression for spatial Gibbs point processes
dc.typeJournal Article
dcterms.source.volume101
dcterms.source.number2
dcterms.source.startPage377
dcterms.source.endPage392
dcterms.source.issn0006-3444
dcterms.source.titleBiometrika
curtin.accessStatusFulltext not available


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