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020 ▼a 9780438290761
035 ▼a (MiAaPQ)AAI10825114
035 ▼a (MiAaPQ)ucdavis:17916
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 310
1001 ▼a Dai, Xiongtao.
24510 ▼a Principal Component Analysis for Riemannian Functional Data and Bayes Classification.
260 ▼a [S.l.] : ▼b University of California, Davis., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 99 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
500 ▼a Adviser: Hans-Georg Mueller.
5021 ▼a Thesis (Ph.D.)--University of California, Davis, 2018.
520 ▼a Functional data, or samples of smooth random functions observed over a continuum, have drawn extensive interest over the past 20 years. Classical linear functional data have been modeled in infinite-dimensional Hilbert spaces, where the infinite
520 ▼a We consider an intrinsic Riemannian functional principal component analysis (RFPCA) for smooth Riemannian manifold-valued functional data. RFPCA is carried out by first mapping the manifold-valued data through Riemannian logarithm maps to linear
520 ▼a Constructing Bayes classifiers for infinite dimensional functional data is difficult due to the fact that probability density functions do not exist for functional data. We approach this problem by considering density ratios of projections on a
590 ▼a School code: 0029.
650 4 ▼a Statistics.
690 ▼a 0463
71020 ▼a University of California, Davis. ▼b Statistics.
7730 ▼t Dissertation Abstracts International ▼g 80-01B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0029
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998734 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자