Autor(es)

Raquel, Gabriela C. ; Conceicao, Katiane S. ; Prates, Marcos O. ; Andrade, Marinho G.

• Tipo de documento: Artigo Científico
• Fonte: JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION; v. 91, n. 12, p. 18-pg., 2021-03-10.

Resumo

In this paper, we present an extension of the Poisson Zero-Modified model with Normal and Generalized Log-Gamma random effects. The random effect induces correlation and accommodate the intrinsic variability of each individual. The Generalized Log-Gamma effect is a generalized Normal effect and can be used in atypical situations where the Normal effect is not appropriate. In particular, the mixed Zero-Modified Poisson model allows us to deal with longitudinal count data, without requiring any previous knowledge about data characteristics, mainly to the number of zero observations (zero-inflated or zero-deflated). We consider the maximum likelihood approach to estimate the model parameters. A simulation study is presented to evaluate the estimators' performance. A real data set referring to the number of notification of infant deaths in the municipalities of the state of Bahia/Brazil is analyzed. The results revealed the Generalized Log-Gamma effect seems to be more appropriate to model this longitudinal data set. (AU)

• Processo FAPESP: 19/22412-5 - Mútiplos pontos de modificação em modelos para dados discretos
• Beneficiário: Katiane Silva Conceição
• Modalidade de apoio: Bolsas no Exterior - Pesquisa
• Processo FAPESP: 19/21766-8 - Modelos para séries temporais com dados discretos
• Beneficiário: Marinho Gomes de Andrade Filho
• Modalidade de apoio: Bolsas no Exterior - Pesquisa