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Analysis of Zero‐Inflated Poisson Data Incorporating Extent of Exposure
Author(s) -
Lee Andy H.,
Wang Kui,
Yau Kelvin K.W.
Publication year - 2001
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/1521-4036(200112)43:8<963::aid-bimj963>3.0.co;2-k
Subject(s) - poisson regression , zero inflated model , poisson distribution , zero (linguistics) , count data , mathematics , statistics , monte carlo method , data set , econometrics , inflation (cosmology) , sample (material) , overdispersion , statistical physics , physics , medicine , population , linguistics , philosophy , thermodynamics , environmental health , theoretical physics
When analyzing Poisson count data sometimes a high frequency of extra zeros is observed. The Zero‐Inflated Poisson (ZIP) model is a popular approach to handle zero‐inflation. In this paper we generalize the ZIP model and its regression counterpart to accommodate the extent of individual exposure. Empirical evidence drawn from an occupational injury data set confirms that the incorporation of exposure information can exert a substantial impact on the model fit. Tests for zero‐inflation are also considered. Their finite sample properties are examined in a Monte Carlo study.