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Nonparametric Bayesian functional two-part random effects model for longitudinal semicontinuous data analysis

Authors
Park, JinsuChoi, TaeryonChung, Yeonseung
Issue Date
4월-2021
Publisher
WILEY
Keywords
Dirichlet process mixture; functional covariate; longitudinal semicontinuous data; model& #8208; based clustering; two& #8208; part random effects model
Citation
BIOMETRICAL JOURNAL, v.63, no.4, pp.787 - 805
Indexed
SCIE
SCOPUS
Journal Title
BIOMETRICAL JOURNAL
Volume
63
Number
4
Start Page
787
End Page
805
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/128374
DOI
10.1002/bimj.201900280
ISSN
0323-3847
Abstract
Longitudinal semicontinuous data, characterized by repeated measures of a large portion of zeros and continuous positive values, are frequently encountered in many applications including biomedical, epidemiological, and social science studies. Two-part random effects models (TPREM) have been used to investigate the association between such longitudinal semicontinuous data and covariates accounting for the within-subject correlation. The existing TPREM is, however, limited to incorporate a functional covariate, which is often available in a longitudinal study. Moreover, the existing TPREM typically assumes the normality of subject-specific random effects, which can be easily violated when there exists a subgroup structure. In this article, we propose a nonparametric Bayesian functional TPREM to assess the relationship between the longitudinal semicontinuous outcome and various types of covariates including a functional covariate. The proposed model also relaxes the normality assumption for the random effects through a Dirichlet process mixture of normals, which allows for identifying an underlying subgroup structure. The methodology is illustrated through an application to social insurance expenditure data collected by the Korean Welfare Panel Study and a simulation study.
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Choi, Tae ryon
정경대학 (통계학과)
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