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020 ▼a 9780438370951
035 ▼a (MiAaPQ)AAI10840888
035 ▼a (MiAaPQ)uchicago:14487
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 574
1001 ▼a Lin, Xiaolei.
24510 ▼a Ecological Momentary Assessment (EMA) Data: Statistical Methods for Heterogeneous Variance, Missing Data and Latent State Classification.
260 ▼a [S.l.] : ▼b The University of Chicago., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 129 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
500 ▼a Adviser: Donald Hedeker.
5021 ▼a Thesis (Ph.D.)--The University of Chicago, 2018.
520 ▼a Ecological 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.
520 ▼a Statistical 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
520 ▼a The 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
520 ▼a All 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
590 ▼a School code: 0330.
650 4 ▼a Biostatistics.
690 ▼a 0308
71020 ▼a The University of Chicago. ▼b Public Health Sciences.
7730 ▼t Dissertation Abstracts International ▼g 80-01B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0330
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999756 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자