LDR | | 02022nmm uu200397 4500 |
001 | | 000000333778 |
005 | | 20240805174524 |
008 | | 181129s2018 |||||||||||||||||c||eng d |
020 | |
▼a 9780438290761 |
035 | |
▼a (MiAaPQ)AAI10825114 |
035 | |
▼a (MiAaPQ)ucdavis:17916 |
040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
082 | 0 |
▼a 310 |
100 | 1 |
▼a Dai, Xiongtao. |
245 | 10 |
▼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. |
502 | 1 |
▼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 |
710 | 20 |
▼a University of California, Davis.
▼b Statistics. |
773 | 0 |
▼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 |
856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998734
▼n KERIS |
980 | |
▼a 201812
▼f 2019 |
990 | |
▼a 관리자 |