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020 ▼a 9780438006034
035 ▼a (MiAaPQ)AAI10825328
035 ▼a (MiAaPQ)ucla:16781
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0491 ▼f DP
0820 ▼a 574
1001 ▼a Li, Qian.
24510 ▼a Hierarchical Integration of Heterogeneous Highly Structured Data: The Case of Functional Brain Imaging.
260 ▼a [S.l.] : ▼b University of California, Los Angeles., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 117 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
500 ▼a Adviser: Donatello Telesca.
5021 ▼a Thesis (Ph.D.)--University of California, Los Angeles, 2018.
520 ▼a Functional brain imaging technologies produce high dimensional data with structured dependency spanning along multiple dimensions. This dissertation focuses on the specific case of Electroencephalography (EEG), even though most methodological de
590 ▼a School code: 0031.
650 4 ▼a Biostatistics.
650 4 ▼a Statistics.
690 ▼a 0308
690 ▼a 0463
71020 ▼a University of California, Los Angeles. ▼b Biostatistics 0132.
7730 ▼t Dissertation Abstracts International ▼g 79-10B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0031
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998755 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자 ▼b 관리자