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Theory and Algorithms for Penalization, Graphical Models, and Surrogate Marker Evaluation
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Theory and Algorithms for Penalization, Graphical Models, and Surrogate Marker Evaluation. |
| 개인저자 | Zhuang, Rui. |
| 단체저자명 | University of Washington. Biostatistics - Public Health. |
| 발행사항 | [S.l.]: University of Washington., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 152 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-04B. Dissertation Abstract International |
| ISBN | 9781687947123 |
| 학위논문주기 | Thesis (Ph.D.)--University of Washington, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Advisor: Lederer, Johannes |
| 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 study three problems: oracle inequality in high-dimensional statistics theory, graphical models, and surrogate measures in clinical trials. First, we introduce a general slow rate bound for maximum regularized likelihood estimators in Kullback-Leibler divergence. The result applies to a wide variety of models and estimators where the densities have a convex parametrization, and the regularization is definite and positively homogenous. Next, we introduce a general framework, the so-called exponential trace models, for undirected graphical models. We employ a sampling-based approximation algorithm to compute the maximum likelihood estimator. The models apply to a wide range of data, such as continuous, discrete, and different combinations of those. Finally, we review the primary frameworks of surrogate measures and propose two new ones, the population surrogacy fraction of treatment effect and time-varying F-measure. The new measures complement the existing statistical framework and apply to the HIV Prevention Trial Network 052 Study. |
| 일반주제명 | Biostatistics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
