Bayesian nonparametric latent class model for longitudinal data = 베이지안 비모수 잠재클래스 모형을 활용한 종단데이터 분석

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Latent class models have been widely used in longitudinal studies to uncover unobserved heterogeneity in a population and find baseline characteristics of latent classes simultaneously by using the class allocation probabilities dependent on predictors. However, uncertainty in the choice of the number of classes is a well-known issue on previous latent class models for longitudinal data. To address this issue, we propose a Bayesian nonparametric latent class model for longitudinal data. The proposed model is an infinite mixture model with predictor-dependent class allocation probabilities. An individual longitudinal trajectory is described by the class-specific linear mixed effects model. The model parameters are estimated using Markov chain Monte Carlo methods. The proposed model is validated using a simulated example and a real-data example of characterizing latent classes of estradiol trajectories over the menopausal transition using data from the Study of Women's Across the Nation.
Advisors
Kim, Heeyoungresearcher김희영researcher
Description
한국과학기술원 :산업및시스템공학과,
Publisher
한국과학기술원
Issue Date
2019
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 산업및시스템공학과, 2019.2,[iii, 31 p. :]

Keywords

Mixure model▼apredictor-dependent clustering▼adirichlet process▼abayesian analysis▼astudy of women's across the nation; 혼합모형▼a예측변수 종속적 클러스터링▼a디리클레 확률과정▼a베이지안 분석▼a전국여성건강연구

URI
http://hdl.handle.net/10203/266235
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=843194&flag=dissertation
Appears in Collection
IE-Theses_Master(석사논문)
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