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020 ▼a 9780438325142
035 ▼a (MiAaPQ)AAI10821670
035 ▼a (MiAaPQ)berkeley:17940
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 310
1001 ▼a Wei, Yuting.
24512 ▼a A Geometric Perspective on Some Topics in Statistical Learning.
260 ▼a [S.l.] : ▼b University of California, Berkeley., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 188 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
500 ▼a Advisers: Martin J. Wainwright
5021 ▼a Thesis (Ph.D.)--University of California, Berkeley, 2018.
520 ▼a Modern science and engineering often generate data sets with a large sample size and a comparably large dimension which puts classic asymptotic theory into question in many ways. Therefore, the main focus of this thesis is to develop a fundament
520 ▼a Our treatment of these different problems shares the common theme of emphasizing the underlying geometric structure. To be more specific, in our hypothesis testing problem, the null and alternative are specified by a pair of convex cones. This c
520 ▼a These results demonstrate that, on one hand, one can benefit from respecting and making use of the underlying structure (optimal early stopping rule for different RKHS)
520 ▼a To evaluate the behavior of any statistical procedure, we follow the classic minimax framework and also discuss about more refined notion of local minimaxity.
590 ▼a School code: 0028.
650 4 ▼a Statistics.
690 ▼a 0463
71020 ▼a University of California, Berkeley. ▼b Statistics.
7730 ▼t Dissertation Abstracts International ▼g 80-01B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0028
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998401 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자