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Ecological Momentary Assessment (EMA) Data: Statistical Methods for Heterogeneous Variance, Missing Data and Latent State Classification

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개인저자Lin, Xiaolei.
단체저자명The University of Chicago. Public Health Sciences.
서명/저자사항Ecological Momentary Assessment (EMA) Data: Statistical Methods for Heterogeneous Variance, Missing Data and Latent State Classification.
발행사항[S.l.] : The University of Chicago., 2018
발행사항Ann Arbor : ProQuest Dissertations & Theses, 2018
형태사항129 p.
소장본 주기School code: 0330.
ISBN9780438370951
일반주기 Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
Adviser: Donald Hedeker.
요약Ecological Momentary Assessment (EMA) studies collect self-reported activities, behaviors and emotions intensively throughout the entire study span, and provide valuable information about how subjects' psychological activities evolve over time.
요약Statistical methodologies investigating the associations between risk factors and mood regulation in EMA studies have not been studied thoroughly, and there is recent evidence that mood variability, together with mood assessment level, are impor
요약The methods developed in this dissertation were motivated by an EMA adolescent mood study. First, a three level mixed effect location scale model that includes multiple random subject and wave effects in both the mean and within variance model w
요약All models in the above studies were estimated via Bayesian sampling framework by Stan. The model estimation procedures are computational more efficient compared to the maximum likelihood based methods. Extensive simulation studies were conducte
일반주제명Biostatistics.
언어영어
기본자료 저록Dissertation Abstracts International80-01B(E).
Dissertation Abstract International
대출바로가기http://www.riss.kr/pdu/ddodLink.do?id=T14999756

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