MARC보기
LDR01774nmm uu200385 4500
001000000333143
00520240805172257
008181129s2018 |||||||||||||||||c||eng d
020 ▼a 9780438122598
035 ▼a (MiAaPQ)AAI10812927
035 ▼a (MiAaPQ)ucsd:17384
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 310
1001 ▼a Guo, Jiaqi.
24510 ▼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.
5021 ▼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
71020 ▼a University of California, San Diego. ▼b Mathematics.
7730 ▼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
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998045 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자