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Hurdle models for multilevel zero-inflated data via h-likelihood

Authors

  • Marek Molas,

    1. Department of Biostatistics, Erasmus MC, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands
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  • Emmanuel Lesaffre

    Corresponding author
    1. Department of Biostatistics, Erasmus MC, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands
    2. Catholic University of Leuven, L-Biostat, U.Z. St. Rafael, Kapucijnenvoer 35, 3000 Leuven, Belgium
    • Department of Biostatistics, Erasmus MC, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands
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Abstract

Count data often exhibit overdispersion. One type of overdispersion arises when there is an excess of zeros in comparison with the standard Poisson distribution. Zero-inflated Poisson and hurdle models have been proposed to perform a valid likelihood-based analysis to account for the surplus of zeros. Further, data often arise in clustered, longitudinal or multiple-membership settings. The proper analysis needs to reflect the design of a study. Typically random effects are used to account for dependencies in the data. We examine the h-likelihood estimation and inference framework for hurdle models with random effects for complex designs. We extend the h-likelihood procedures to fit hurdle models, thereby extending h-likelihood to truncated distributions. Two applications of the methodology are presented. Copyright © 2010 John Wiley & Sons, Ltd.

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