Fwd: [Todos-l] Seminários de Estatística e Ciência de Dados - Departamento de Estatística (IME/UFBA)
SONIA MARIA DA SILVA GOMES Gomes
Sonia Maria da Silva Gomes, Dra.
Professora Titular
Faculdade de Ciências Contábeis
Universidade Federal da Bahia
Av. Reitor Miguel Calmon, s/n - Vale do Canela
CEP 40.110-903 - Salvador/Bahia/Brasil.
Telefone: 55 (71) 3283-8773 ou 988003959
De: Estatistica <de...@ufba.br>
Date: qui., 5 de mai. de 2022 às 11:01
Subject: [Todos-l] Seminários de Estatística e Ciência de Dados - Departamento de Estatística (IME/UFBA)
To: <tod...@listas.ufba.br>
Segue abaixo os detalhes sobre o seminário!
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Title: Modelling Point Referenced Spatial Count Data: A Poisson Process Approach
Random fields are useful mathematical tools for representing natural phenomena with complex dependence structures in space and/or time. In particular, the Gaussian random field is commonly used due to its attractive properties and mathematical tractability. However, this assumption seems to be restrictive when dealing with counting data. To deal with this situation, we propose a random field with a Poisson marginal distribution by considering a sequence of independent copies of a random field with an exponential marginal distribution as 'inter-arrival times' in the counting renewal processes framework. Our proposal can be viewed as a spatial generalization of the Poisson counting process. Unlike the classical hierarchical Poisson Log-Gaussian model, our proposal generates a (non)-stationary random field that is mean square continuous and with Poisson marginal distributions. For the proposed Poisson spatial random field, analytic expressions for the covariance function and the bivariate distribution are provided. In an extensive simulation study, we investigate the weighted pairwise likelihood as a method for estimating the Poisson random field parameters.
Finally, the effectiveness of our methodology is illustrated by an analysis of reindeer pellet-group survey data, where a zero-inflated version of the proposed model is compared with zero-inflated Poisson Log-Gaussian and Poisson Gaussian copula models.
Joint work with D. Morales, M. Bevilacqua and C. Caamaño.
Dia: 05 de maio de 2022
Hora: 14h00
Link: https://meet.google.com/djy-avfu-yub
Divulguem! Participem!
Chefe do Departamento de Estatística
Universidade Federal da Bahia