Count time series with excess zeros: A Bayesian perspective using zero-adjusted distributions
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...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | Brasil |
| Institución: | Universidade Estadual de Londrina (UEL) |
| Repositorio: | Revista Semina: Ciências Exatas e Tecnológicas (Online) |
| Idioma: | inglés |
| OAI Identifier: | oai:ojs2.ojs.uel.br:article/46036 |
| Acceso en línea: | https://ojs.uel.br/revistas/uel/index.php/semexatas/article/view/46036 |
| Access Level: | acceso abierto |
| Palabra clave: | Processo ARMA(p, q) Dados de contagem Metropolis-hastings Regressão e Correlação Análise de Dados Inferência Paramétrica ARMA(p, q) process Count data |
| Sumario: | 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. |
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