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

Keunbaik Lee, Yongsung Joo, Joon Jin Song and Dee Wood Harper

Computational Statistics & Data Analysis, 2011, vol. 55, issue 1, 824-837

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.

Keywords: Hurdle; models; ZIP; models; Random; effects; Quasi-Newton (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (4)

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