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Pré-publication, Document de travail

A spatio-temporal multi-scale model for Geyer saturation point process: application to forest fire occurrences

Abstract : Since most natural phenomena exhibit dependence at multiple scales (e.g. earthquake and forest fire occurrences), single-scale spatio-temporal Gibbs models are unrealistic in many applications. This motivates statisticians to construct the multi-scale generalizations of the classical Gibbs models and to develop new Gibbs point process models. In this paper, we extend the spatial multi-scale Geyer point process model to the spatio-temporal framework. The model is implemented using the birth-death Metropolis-Hastings algorithm in R. In a simulation study, we compare the common methods for statistical inference in Gibbs models, as the pseudo-likelihood and logistic likelihood approaches. Finally, we fit this new model to a forest fire dataset in France.
Type de document :
Pré-publication, Document de travail
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https://hal.inrae.fr/hal-02917339
Déposant : Edith Gabriel <>
Soumis le : mercredi 19 août 2020 - 09:27:59
Dernière modification le : jeudi 20 août 2020 - 03:13:45

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  • HAL Id : hal-02917339, version 1
  • ARXIV : 1911.06999

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Morteza Raeisi, Florent Bonneu, Edith Gabriel. A spatio-temporal multi-scale model for Geyer saturation point process: application to forest fire occurrences. 2020. ⟨hal-02917339⟩

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