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020 ▼a 9780438169227
035 ▼a (MiAaPQ)AAI10825616
035 ▼a (MiAaPQ)umn:19261
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0491 ▼f DP
0820 ▼a 004
1001 ▼a Chen, Sheng.
24510 ▼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.
5021 ▼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
71020 ▼a University of Minnesota. ▼b Computer Science.
7730 ▼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
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998787 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자 ▼b 관리자