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020 ▼a 9780438034242
035 ▼a (MiAaPQ)AAI10786439
035 ▼a (MiAaPQ)unc:17589
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0491 ▼f DP
0820 ▼a 310
1001 ▼a Cui, Yifan.
24510 ▼a Tree-based Survival Models and Precision Medicine.
260 ▼a [S.l.] : ▼b The University of North Carolina at Chapel Hill., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 124 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
500 ▼a Advisers: Michael Rene Kosorok
5021 ▼a Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2018.
520 ▼a Random forests have become one of the most popular machine learning tools in recent years. The main advantage of tree- and forest-based models is their nonparametric nature. My dissertation mainly focuses on a particular type of tree and forest
520 ▼a We first carry out a comprehensive analysis of survival random forest and tree models and show the consistency of these popular machine learning models by developing a general theoretical framework. Our results significantly improve the current
520 ▼a In the second part, motivated by tree-based survival models, we propose a fiducial approach to provide pointwise and curvewise confidence intervals for the survival functions. On each terminal node, the estimation is essentially a small sample a
520 ▼a In the third topic, we show one application of tree-based survival models in precision medicine. We extend the outcome weighted learning to right censored survival data without requiring either inverse probability of censoring weighting or semi-
590 ▼a School code: 0153.
650 4 ▼a Statistics.
690 ▼a 0463
71020 ▼a The University of North Carolina at Chapel Hill. ▼b Statistics and Operations Research.
7730 ▼t Dissertation Abstracts International ▼g 79-10B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0153
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997352 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자 ▼b 관리자