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

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

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

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.

Original languageEnglish
Pages (from-to)824-837
Number of pages14
JournalComputational Statistics and Data Analysis
Volume55
Issue number1
DOIs
StatePublished - 1 Jan 2011

Keywords

  • Hurdle models
  • Quasi-Newton
  • Random effects
  • ZIP models

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