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Discrete data time series models

Grant number: 19/21766-8
Support type:Scholarships abroad - Research
Effective date (Start): February 02, 2020
Effective date (End): February 01, 2021
Field of knowledge:Physical Sciences and Mathematics - Probability and Statistics - Applied Probability and Statistics
Principal researcher:Marinho Gomes de Andrade Filho
Grantee:Marinho Gomes de Andrade Filho
Host: Nalini Ravishanker
Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Research place: University of Connecticut (UCONN), United States  
Associated research grant:13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry, AP.CEPID


This project aims to develop Zero-Modified (e.g. Inflated or Zero Deflated) models for time series with discrete data. The models developed in this project originate from some of the Zero-Modified models of the Power Series family (Poisson, Generalized Poisson, Binomial, Negative Binomial, COM-Poisson among others). In this work these models will be defined in the context of time series to accommodate time correlated count data with excess (or deficit) zeros. Inference methods based on the classical likelihood approach and the Bayesian approach together with Markov Chain Monte Carlo techniques will be considered. The project also provides relevant applications on real problems. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
CONCEICAO, KATIANE S.; SUZUKI, ADRIANO K.; ANDRADE, MARINHO G. A Bayesian approach for zero-modified Skellam model with Hamiltonian MCMC. Statistical Methods and Applications, JUL 2020. Web of Science Citations: 0.

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