Full Citation

Title: Generalized zero-inated Poisson regression mixture model for fitting health-related data

Citation Type: Journal Article

Publication Year: 2022

ISBN:

ISSN: 1598-9402

DOI: 10.7465/jkdi.2022.33.1.139

NSFID:

PMCID:

PMID:

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-in ated Poisson (ZIP) regression model do not fit well. Generalized zero-in ated 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.

Url: https://doi.org/10.7465/jkdi.2022.33.1.139

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Authors: Cho, Yoojung; Hwang, Beom-Seuk

Periodical (Full): Journal of the Korean Data And Information Science Society

Issue: 1

Volume: 33

Pages: 139-152

Data Collections: IPUMS USA

Topics: Methodology and Data Collection

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