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020 ▼a 9781687947123
035 ▼a (MiAaPQ)AAI22620232
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 247004
0820 ▼a 574
1001 ▼a Zhuang, Rui.
24510 ▼a Theory and Algorithms for Penalization, Graphical Models, and Surrogate Marker Evaluation.
260 ▼a [S.l.]: ▼b University of Washington., ▼c 2019.
260 1 ▼a Ann Arbor: ▼b ProQuest Dissertations & Theses, ▼c 2019.
300 ▼a 152 p.
500 ▼a Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
500 ▼a Advisor: Lederer, Johannes
5021 ▼a Thesis (Ph.D.)--University of Washington, 2019.
506 ▼a This item must not be sold to any third party vendors.
506 ▼a This item must not be added to any third party search indexes.
520 ▼a 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.
590 ▼a School code: 0250.
650 4 ▼a Biostatistics.
690 ▼a 0308
71020 ▼a University of Washington. ▼b Biostatistics - Public Health.
7730 ▼t Dissertations Abstracts International ▼g 81-04B.
773 ▼t Dissertation Abstract International
790 ▼a 0250
791 ▼a Ph.D.
792 ▼a 2019
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T15493699 ▼n KERIS ▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다.
980 ▼a 202002 ▼f 2020
990 ▼a ***1008102
991 ▼a E-BOOK