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Inference for High-dimensional Left-censored Linear Model and High-dimensional Precision Matrix
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Inference for High-dimensional Left-censored Linear Model and High-dimensional Precision Matrix. |
| 개인저자 | Guo, Jiaqi. |
| 단체저자명 | University of California, San Diego. Mathematics. |
| 발행사항 | [S.l.]: University of California, San Diego., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 192 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-11B(E). Dissertation Abstract International |
| ISBN | 9780438122598 |
| 학위논문주기 | Thesis (Ph.D.)--University of California, San Diego, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
Adviser: Jelena Bradic. |
| 요약 | 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 |
| 요약 | 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, |
| 일반주제명 | Statistics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
