Estimation for zero-inflated over-dispersed count data model with missing response

In this paper, we develop estimation procedure for the parameters of a zero‐inflated over‐dispersed/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 expectat...

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Bibliographic Details
Published in:Statistics in medicine Vol. 35; no. 30; pp. 5603 - 5624
Main Authors: Mian, Rajibul, Paul, Sudhir
Format: Journal Article
Language:English
Published: England Blackwell Publishing Ltd 30.12.2016
Wiley Subscription Services, Inc
Subjects:
Algorithms
count data
EM algorithm
Estimating techniques
Humans
Likelihood Functions
Medical statistics
Models, Statistical
over dispersion
Poisson Distribution
regression model
Simulation
zero inflation
regression model
over dispersion
EM algorithm
count data
zero inflation
ISSN:0277-6715, 1097-0258
Online Access:Get full text
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Summary:In this paper, we develop estimation procedure for the parameters of a zero‐inflated over‐dispersed/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 is used for the maximum likelihood estimation of the parameters involved. Some simulations are conducted to study the properties of the estimators. Robustness of the procedure is shown when count data follow other over‐dispersed models, such as the log‐normal mixture of the Poisson distribution or even from a zero‐inflated Poisson model. An illustrative example and a discussion leading to some conclusions are given. Copyright © 2016 John Wiley & Sons, Ltd.
Bibliography:Supporting info item
ark:/67375/WNG-J9VP5FK3-L
ArticleID:SIM7079
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ISSN:0277-6715
1097-0258
DOI:10.1002/sim.7079