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

作者：Lee Keunbaik; Joo Yongsung; Song Joon Jin^*; Harper Dee Wood

来源：Computational Statistics & Data Analysis, 2011, 55(1): 824-837.

DOI：10.1016/j.csda.2010.07.005

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

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.

• 出版日期2011-1-1