| LDR | | 00000nmm u2200205 4500 |
| 001 | | 000000331789 |
| 005 | | 20241120142556 |
| 008 | | 181129s2018 ||| | | | eng d |
| 020 | |
▼a 9780438350939 |
| 035 | |
▼a (MiAaPQ)AAI10825874 |
| 035 | |
▼a (MiAaPQ)umn:19271 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 049 | 1 |
▼f DP |
| 082 | 0 |
▼a 310 |
| 100 | 1 |
▼a Wu, Chong. |
| 245 | 10 |
▼a Statistical Methods for High-dimensional Genetic and Genomic Data. |
| 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 Advisers: Weihua Guan |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Minnesota, 2018. |
| 520 | |
▼a Modern genetics research constantly creates new types of high-dimensional genetic and genomic data and imposes new challenges in analyzing these data. This thesis deals with several important problems in analyzing high-dimensional genetic and ge |
| 520 | |
▼a First, we introduce a site selection and multiple imputation method to impute missing data in covariates in epigenome-wide analysis of DNA methylation data, which can help us adjust potential confounders, such as cell type composition. Second, t |
| 590 | |
▼a School code: 0130. |
| 650 | 4 |
▼a Statistics. |
| 650 | 4 |
▼a Genetics. |
| 650 | 4 |
▼a Bioinformatics. |
| 690 | |
▼a 0463 |
| 690 | |
▼a 0369 |
| 690 | |
▼a 0715 |
| 710 | 20 |
▼a University of Minnesota.
▼b Biostatistics. |
| 773 | 0 |
▼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 |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998813
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자
▼b 관리자 |