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Activity Number: 581 - Advancement in Theoretical and Applied Aspects of Modeling
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract #304443
Title: Zero-Inflated Count Time Series Regression Models
Author(s): Mohammed Alqawba* and Norou Diawara and Rao Chaganty
Companies: and Old Dominion University and Old Dominion University
Keywords: Zero-inflation; Count time series; Gaussian copula; Poisson; Conway-Maxwell-Poisson; Sequential Importance sampling

Count time series data are frequent in many applied disciplines. In describing them, a specific count may reveal more often than usual. In faming a modeling approach, one must account for the excess count. In this paper, we develop a copula-based time series model for zero-inflated counts with the presence of covariates. Zero-inflated Poisson (ZIP), zero-inflated negative Binomial (ZINB), and zero-inflated Conway-Maxwell-Poisson (ZICMP) distributed marginals will be considered, while the joint distribution is modeled under Gaussian copula with autoregression moving average (ARMA) errors. Likelihood is formulated for inference, under sequential inference method. A simulated study is conducted, and a practical application in environmental setting is described.

Authors who are presenting talks have a * after their name.

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