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Semiparametric Quantile Regression and Applications to Healthcare Data Analysis
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| 자료유형 | E-Book |
|---|---|
| 개인저자 | Maidman, Adam. |
| 단체저자명 | University of Minnesota. Statistics. |
| 서명/저자사항 | Semiparametric Quantile Regression and Applications to Healthcare Data Analysis. |
| 발행사항 | [S.l.] : University of Minnesota., 2018 |
| 발행사항 | Ann Arbor : ProQuest Dissertations & Theses, 2018 |
| 형태사항 | 123 p. |
| 소장본 주기 | School code: 0130. |
| ISBN | 9780438351325 |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
Adviser: Lan Wang. |
| 요약 | The ubiquity of healthcare data allows for complex analyses of a variety of topics ranging from healthcare cost to cognitive decline in dementia patients. Healthcare datasets are often highly skewed and heteroskedastic posing great challenges fo |
| 요약 | Motivated by the Medical Expenditure Panel Survey containing data from individuals' medical providers and employers across the United States, we propose a new semiparametric procedure for predicting whether a patient will incur high medical expe |
| 요약 | Next, we extend semiparametric quantile regression methodology to longitudinal studies with non-ignorable dropout. Dropout occurs when a patient leaves a study prior to its conclusion. Non-ignorable dropout occurs when the probability of dropout |
| 요약 | Lastly, we turn our attention to the issue of analyzing very large datasets with a large number of covariates and sample size. Penalized quantile regression is often used to simultaneously select variables and estimate effects by fitting models |
| 일반주제명 | Statistics. |
| 언어 | 영어 |
| 기본자료 저록 | Dissertation Abstracts International80-01B(E). Dissertation Abstract International |
| 대출바로가기 | http://www.riss.kr/pdu/ddodLink.do?id=T14999446 |
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| No. | 등록번호 | 청구기호 | 소장처 | 도서상태 | 반납예정일 | 예약 | 서비스 | 매체정보 |
|---|---|---|---|---|---|---|---|---|
| 1 | WE00025293 | DP 310 | 가야대학교/전자책서버(컴퓨터서버)/ | 대출불가(별치) |
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