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Classified Functional Mixed Effects Model Prediction and Its Application
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Classified Functional Mixed Effects Model Prediction and Its Application. |
| 개인저자 | Liu, Xiaoyan. |
| 단체저자명 | University of California, Davis. Biostatistics. |
| 발행사항 | [S.l.]: University of California, Davis., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 63 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-06B. Dissertation Abstract International |
| ISBN | 9781392487976 |
| 학위논문주기 | Thesis (Ph.D.)--University of California, Davis, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-06, Section: B.
Advisor: Jiang, Jiming. |
| 이용제한사항 | This item must not be sold to any third party vendors.This item must not be added to any third party search indexes. |
| 요약 | In this dissertation, we developed a classified functional mixed model prediction (CFMMP), a method that adapts Classified Mixed Model Prediction (CMMP), which is a recently proposed method that classifies a new group of observations into one of the existing groups in a training data set based on mixed effects in a linear mixed effects model, to the framework of functional mixed effects model (FMEM). This dissertation mainly consists of two parts. The first part includes selected literature review on FMEM, mixed effects model classifications. In the second part of the dissertation, we discuss details of CFMMP, including development of methodology, evaluation of performance of CFMMP against functional regression prediction based on simulation studies, and exploration of the convergence property of CFMMP estimators. Finally, real-world applications of CFMMP were illustrated using menstrual cycle data and ovarian cancer mass spectrometry. |
| 일반주제명 | Biostatistics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
