| LDR | | 01998nmm uu200397 4500 |
| 001 | | 000000331810 |
| 005 | | 20240805165420 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438169227 |
| 035 | |
▼a (MiAaPQ)AAI10825616 |
| 035 | |
▼a (MiAaPQ)umn:19261 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 082 | 0 |
▼a 004 |
| 100 | 1 |
▼a Chen, Sheng. |
| 245 | 10 |
▼a Computational and Statistical Aspects of High-dimensional Structured Estimation. |
| 260 | |
▼a [S.l.] :
▼b University of Minnesota.,
▼c 2018 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
| 300 | |
▼a 272 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. |
| 500 | |
▼a Adviser: Arindam Banerjee. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Minnesota, 2018. |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 590 | |
▼a School code: 0130. |
| 650 | 4 |
▼a Computer science. |
| 690 | |
▼a 0984 |
| 710 | 20 |
▼a University of Minnesota.
▼b Computer Science. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-12B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0130 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998787
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자 |