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Hierarchical Integration of Heterogeneous Highly Structured Data: The Case of Functional Brain Imaging
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Hierarchical Integration of Heterogeneous Highly Structured Data: The Case of Functional Brain Imaging. |
| 개인저자 | Li, Qian. |
| 단체저자명 | University of California, Los Angeles. Biostatistics 0132. |
| 발행사항 | [S.l.]: University of California, Los Angeles., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 117 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-10B(E). Dissertation Abstract International |
| ISBN | 9780438006034 |
| 학위논문주기 | Thesis (Ph.D.)--University of California, Los Angeles, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Adviser: Donatello Telesca. |
| 요약 | 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 |
| 일반주제명 | Biostatistics. Statistics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
