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Multilevel zero-inflated negative binomial regression modeling for over-dispersed count data with extra zeros

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  • Abbas Moghimbeigi
  • Mohammed Reza Eshraghian
  • Kazem Mohammad
  • Brian Mcardle

Abstract

Count data with excess zeros often occurs in areas such as public health, epidemiology, psychology, sociology, engineering, and agriculture. Zero-inflated Poisson (ZIP) regression and zero-inflated negative binomial (ZINB) regression are useful for modeling such data, but because of hierarchical study design or the data collection procedure, zero-inflation and correlation may occur simultaneously. To overcome these challenges ZIP or ZINB may still be used. In this paper, multilevel ZINB regression is used to overcome these problems. The method of parameter estimation is an expectation-maximization algorithm in conjunction with the penalized likelihood and restricted maximum likelihood estimates for variance components. Alternative modeling strategies, namely the ZIP distribution are also considered. An application of the proposed model is shown on decayed, missing, and filled teeth of children aged 12 years old.

Suggested Citation

  • Abbas Moghimbeigi & Mohammed Reza Eshraghian & Kazem Mohammad & Brian Mcardle, 2008. "Multilevel zero-inflated negative binomial regression modeling for over-dispersed count data with extra zeros," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(10), pages 1193-1202.
    • Handle: RePEc:taf:japsta:v:35:y:2008:i:10:p:1193-1202
    DOI: 10.1080/02664760802273203
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    References listed on IDEAS

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    Cited by:

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