Hierarchical Bayesian modeling of marked non-homogeneous Poisson processes with finite mixtures and inclusion of covariate information

Athanasios Christou Micheas

Journal of Applied Statistics, 2014, vol. 41, issue 12, 2596-2615

Abstract: We investigate marked non-homogeneous Poisson processes using finite mixtures of bivariate normal components to model the spatial intensity function. We employ a Bayesian hierarchical framework for estimation of the parameters in the model, and propose an approach for including covariate information in this context. The methodology is exemplified through an application involving modeling of and inference for tornado occurrences.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:41:y:2014:i:12:p:2596-2615

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DOI: 10.1080/02664763.2014.922167

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