| LDR | | 01850nam u200385 4500 |
| 001 | | 000000421363 |
| 005 | | 20190215165243 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438122598 |
| 035 | |
▼a (MiAaPQ)AAI10812927 |
| 035 | |
▼a (MiAaPQ)ucsd:17384 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 310 |
| 100 | 1 |
▼a Guo, Jiaqi. |
| 245 | 10 |
▼a Inference for High-dimensional Left-censored Linear Model and High-dimensional Precision Matrix. |
| 260 | |
▼a [S.l.]:
▼b University of California, San Diego.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 192 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B. |
| 500 | |
▼a Adviser: Jelena Bradic. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of California, San Diego, 2018. |
| 520 | |
▼a In the first two chapters, we consider inference for high-dimensional left-censored linear models. Left-censored data arises from measurement limits in scientific devices and social science data. We consider the problem of constructing confidenc |
| 520 | |
▼a In Chapter 3, we devise a projection pursuit testing procedure for generalized hypotheses on high-dimensional precision matrix. We illustrate the procedure under specific examples of hypotheses: testing for row sparsity, minimum signal strength, |
| 590 | |
▼a School code: 0033. |
| 650 | 4 |
▼a Statistics. |
| 690 | |
▼a 0463 |
| 710 | 20 |
▼a University of California, San Diego.
▼b Mathematics. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-11B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0033 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998045
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |