Abstract
In many bioscience studies, it is common to encounter count data with a large number of zeros that Poisson regression model or standard zero-inflated Poisson (ZIP) regression model do not fit well. Generalized zero-inflated Poisson (GZIP) regression mixture model can handle the data with excess zeros and overdispersion caused by unobserved heterogeneity. For the parameter estimation, expectation-maximization (EM) algorithm with iteratively reweighted least sqaures (IRLS) method is used. We applied GZIP regression mixture model into two health-related data, Behavioral Risk Factor Surveillance System (BRFSS) data and Integrated Public Use Microdata Series (IPUMS) census data, and compared the performance of the models using AIC and BIC to find the best mixture model.
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