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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of the Korean Data And Information Science Society
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
