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020 ▼a 9780438351325
035 ▼a (MiAaPQ)AAI10830513
035 ▼a (MiAaPQ)umn:19369
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0491 ▼f DP
0820 ▼a 310
1001 ▼a Maidman, Adam.
24510 ▼a Semiparametric Quantile Regression and Applications to Healthcare Data Analysis.
260 ▼a [S.l.] : ▼b University of Minnesota., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 123 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
500 ▼a Adviser: Lan Wang.
5021 ▼a Thesis (Ph.D.)--University of Minnesota, 2018.
520 ▼a 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
520 ▼a 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
520 ▼a 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
520 ▼a 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
590 ▼a School code: 0130.
650 4 ▼a Statistics.
690 ▼a 0463
71020 ▼a University of Minnesota. ▼b Statistics.
7730 ▼t Dissertation Abstracts International ▼g 80-01B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0130
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999446 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자 ▼b 관리자