Analysis of zero-inflated clustered count data: A marginalized model approach

Authors:
Keunbaik Lee;Yongsung Joo;Joon Jin Song;Dee Wood Harper
Affiliations:
Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA 70112, USA;Department of Statistics, Dongguk University-Seoul, Seoul 100-715, Republic of Korea;Department of Mathematical Sciences, University of Arkansas, Fayetteville, AR 72701, USA;Department of Criminal Justice, Loyola University, New Orleans, LA 70118, USA
Venue:
Computational Statistics & Data Analysis
Year:
2011

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Abstract

Min and Agresti (2005) proposed random effect hurdle models for zero-inflated clustered count data with two-part random effects for a binary component and a truncated count component. In this paper, we propose new marginalized models for zero-inflated clustered count data using random effects. The marginalized models are similar to Dobbie and Welsh's (2001) model in which generalized estimating equations were exploited to find estimates. However, our proposed models are based on a likelihood-based approach. A Quasi-Newton algorithm is developed for estimation. We use these methods to carefully analyze two real datasets.