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Computational and Statistical Aspects of High-dimensional Structured Estimation
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Computational and Statistical Aspects of High-dimensional Structured Estimation. |
| 개인저자 | Chen, Sheng. |
| 단체저자명 | University of Minnesota. Computer Science. |
| 발행사항 | [S.l.]: University of Minnesota., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 272 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-12B(E). Dissertation Abstract International |
| ISBN | 9780438169227 |
| 학위논문주기 | Thesis (Ph.D.)--University of Minnesota, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Arindam Banerjee. |
| 요약 | Modern statistical learning often faces high-dimensional data, for which the number of features that should be considered is very large. In consideration of various constraints encountered in data collection, such as cost and time, however, the |
| 요약 | Over the last two decades, sparsity has been one of the most popular structures to exploit when we estimate a high-dimensional parameter, which assumes that the number of nonzero elements in parameter vector/matrix is much smaller than its ambie |
| 요약 | In this thesis, we aim to make progress towards a unified framework for the estimation with general structures, by studying the high-dimensional structured linear model and other semi-parametric and non-convex extensions. In particular, we intro |
| 일반주제명 | Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
