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Dynamic fator Models for bivariate Count Data: an application to fire activity

By Magda Monteiro, Isabel Pereira and Manuel G. Scotto


The study of forest re activity, in its several aspects, is essencial to understand the phenomenon and to prevent environmental public catastrophes. In this context the analysis of monthly number of res along several years is one aspect to have into account in order to better comprehend this tematic. The goal of this work is to analyze the monthly number of forest res in the neighboring districts of Aveiro and Coimbra, Portugal, through dynamic factor models for bivariate count series. We use a bayesian approach, through MCMC methods, to estimate the model parameters as well as to estimate the common latent factor to both series

Topics: Dynamic latent variables; MCMC; bivarite Poisson distribution;
Year: 2016
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