LDR | | 01774nmm uu200385 4500 |
001 | | 000000333143 |
005 | | 20240805172257 |
008 | | 181129s2018 |||||||||||||||||c||eng d |
020 | |
▼a 9780438122598 |
035 | |
▼a (MiAaPQ)AAI10812927 |
035 | |
▼a (MiAaPQ)ucsd:17384 |
040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
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 |
980 | |
▼a 201812
▼f 2019 |
990 | |
▼a 관리자 |