Count time series with excess zeros: A Bayesian perspective using zero-adjusted distributions




Processo ARMA(p, q), Dados de contagem, Metropolis-hastings


Models for count data which are temporally correlated have been studied using many conditional distributions, such as the Poisson distribution, and the insertion of different dependence structures. Nonetheless, excess of zeros and over dispersion may be observed during the counting process and need to be considered when modelling and choosing a conditional distribution. In this paper, we propose models for counting time series using zero-adjusted distributions by inserting a dependence structure following the ARMA(p, q) process on a Bayesian framework. We perform a simulation study using the proposed Bayesian analysis and analyse the monthly time series of the number of deaths due to dengue haemorrhagic fever (ICD-A91) in Brazil.

Author Biographies

Luiz Otávio de Oliveira Pala, Universidade Federal de Lavras - UFLA

PhD student in Statistics and Agricultural Experimentation, UFLA, Lavras, MG

Marcela de Marillac Carvalho, Universidade Federal de Lavras - UFAL

PhD student in Statistics and Agricultural Experimentation, UFLA, Lavras, MG

Thelma Sáfadi, Universidade Federal de Lavras - UFLA

Prof. Dr., Department of Statistics, UFLA, Lavras, MG


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How to Cite

Pala, L. O. de O., Carvalho, M. de M., & Sáfadi, T. (2022). Count time series with excess zeros: A Bayesian perspective using zero-adjusted distributions. Semina: Ciências Exatas E Tecnológicas, 43(2), 147–160.



Original Article

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