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Activity Number: 34
Type: Contributed
Date/Time: Sunday, July 31, 2016 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #319463
Title: Estimation for Zero-Inflated Over-Dispersed Count Data Model with Ignorable Missing Response
Author(s): Sudhir Paul* and Rajibul Mian
Companies: University of Windsor and University of Windsor
Keywords: Count Data ; EM algorithm ; Missing Values ; Over-dispersion ; Regression model ; Zero inflation

In this paper we develop estimation procedure for the parameters of a zero inflated over/under dispersed count model in the presence of missing responses. In particular we deal with a zero inflated extended negative binomial model in the presence of missing responses. A weighted expectation maximization algorithm \cite{Ibrahim:1990} is used for the Maximum likelihood (ML) estimation of the parameters involved. Some simulations are conducted to study the properties of the estimates. Robustness of the procedure is shown when count data follow other over-dispersed models, such as the log-normal mixture of the Poisson distribution. An illustrative example (we use the dental epidemiology data of Bohning (1999} and a discussion leading to some conclusions are given.

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