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Hurdle models for multilevel zero‐inflated data via h‐likelihood
Author(s) -
Molas Marek,
Lesaffre Emmanuel
Publication year - 2010
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3852
Subject(s) - overdispersion , quasi likelihood , count data , zero inflated model , poisson distribution , inference , statistics , restricted maximum likelihood , likelihood function , random effects model , econometrics , mathematics , maximum likelihood , poisson regression , computer science , statistical inference , artificial intelligence , medicine , population , meta analysis , demography , sociology
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.