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

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

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.

Suggested Citation

  • Lee, Keunbaik & Joo, Yongsung & Song, Joon Jin & Harper, Dee Wood, 2011. "Analysis of zero-inflated clustered count data: A marginalized model approach," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 824-837, January.
    • Handle: RePEc:eee:csdana:v:55:y:2011:i:1:p:824-837
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    References listed on IDEAS

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    1. Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December.
    2. Robert Gibbons & R. Bock, 1987. "Trend in correlated proportions," Psychometrika, Springer;The Psychometric Society, vol. 52(1), pages 113-124, March.
    3. Martin Ridout & John Hinde & Clarice G. B. Demétrio, 2001. "A Score Test for Testing a Zero‐Inflated Poisson Regression Model Against Zero‐Inflated Negative Binomial Alternatives," Biometrics, The International Biometric Society, vol. 57(1), pages 219-223, March.
    4. Patrick J. Heagerty, 1999. "Marginally Specified Logistic-Normal Models for Longitudinal Binary Data," Biometrics, The International Biometric Society, vol. 55(3), pages 688-698, September.
    5. Daniel B. Hall, 2000. "Zero-Inflated Poisson and Binomial Regression with Random Effects: A Case Study," Biometrics, The International Biometric Society, vol. 56(4), pages 1030-1039, December.
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    Cited by:

    1. Lee, Keunbaik & Joo, Yongsung, 2019. "Marginalized models for longitudinal count data," Computational Statistics & Data Analysis, Elsevier, vol. 136(C), pages 47-58.
    2. M. Tariqul Hasan & Gary Sneddon & Renjun Ma, 2012. "Regression analysis of zero-inflated time-series counts: application to air pollution related emergency room visit data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(3), pages 467-476, June.
    3. Goto, Satoshi & Takagishi, Mariko & Yadohisa, Hiroshi, 2021. "Clustering for time-varying relational count data," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
    4. Özgür Asar & Ozlem Ilk, 2016. "First-order marginalised transition random effects models with probit link function," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(5), pages 925-942, April.

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