Please use this identifier to cite or link to this item: http://hdl.handle.net/10773/15852
Title: Dynamic fator Models for bivariate Count Data: an application to fire activity
Author: Monteiro, Magda
Pereira, Isabel
Scotto, Manuel G.
Keywords: Dynamic latent variables; MCMC; bivarite Poisson distribution;
Issue Date: Jun-2016
Abstract: 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.
Peer review: yes
URI: http://hdl.handle.net/10773/15852
ISBN: 978-84-608-8178-0
Publisher Version: http://biometria.sgapeio.es
Appears in Collections:CIDMA - Comunicações
ESTGA - Comunicações

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