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

Multilevel zero-inflated negative binomial regression modeling for over-dispersed count data with... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Applied Statistics Taylor & Francis

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

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References (27)

Publisher
Taylor & Francis
Copyright
Copyright Taylor & Francis Group, LLC
ISSN
1360-0532
eISSN
0266-4763
DOI
10.1080/02664760802273203
Publisher site
See Article on Publisher Site

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.

Journal

Journal of Applied StatisticsTaylor & Francis

Published: Oct 1, 2008

Keywords: count data; EM algorithm; multilevel; negative binomial regression; Poisson regression; zero-inflation

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