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Tree-based Survival Models and Precision Medicine
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| 자료유형 | E-Book |
|---|---|
| 개인저자 | Cui, Yifan. |
| 단체저자명 | The University of North Carolina at Chapel Hill. Statistics and Operations Research. |
| 서명/저자사항 | Tree-based Survival Models and Precision Medicine. |
| 발행사항 | [S.l.] : The University of North Carolina at Chapel Hill., 2018 |
| 발행사항 | Ann Arbor : ProQuest Dissertations & Theses, 2018 |
| 형태사항 | 124 p. |
| 소장본 주기 | School code: 0153. |
| ISBN | 9780438034242 |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Advisers: Michael Rene Kosorok |
| 요약 | 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 |
| 요약 | 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 |
| 요약 | 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 |
| 요약 | 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- |
| 일반주제명 | Statistics. |
| 언어 | 영어 |
| 기본자료 저록 | Dissertation Abstracts International79-10B(E). Dissertation Abstract International |
| 대출바로가기 | http://www.riss.kr/pdu/ddodLink.do?id=T14997352 |
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| No. | 등록번호 | 청구기호 | 소장처 | 도서상태 | 반납예정일 | 예약 | 서비스 | 매체정보 |
|---|---|---|---|---|---|---|---|---|
| 1 | WE00026771 | DP 310 | 가야대학교/전자책서버(컴퓨터서버)/ | 대출불가(별치) |
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