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Ecological Momentary Assessment (EMA) Data: Statistical Methods for Heterogeneous Variance, Missing Data and Latent State Classification
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Ecological Momentary Assessment (EMA) Data: Statistical Methods for Heterogeneous Variance, Missing Data and Latent State Classification. |
| 개인저자 | Lin, Xiaolei. |
| 단체저자명 | The University of Chicago. Public Health Sciences. |
| 발행사항 | [S.l.]: The University of Chicago., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 129 p. |
| 기본자료 저록 | Dissertation Abstracts International 80-01B(E). Dissertation Abstract International |
| ISBN | 9780438370951 |
| 학위논문주기 | Thesis (Ph.D.)--The University of Chicago, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
Adviser: Donald Hedeker. |
| 요약 | 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. |
| 요약 | 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 |
| 요약 | 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 |
| 요약 | 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 |
| 일반주제명 | Biostatistics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
