서브메뉴
검색
Novel Methods for Statistical Analysis of Covariance Structures- [electronic resource]
![Novel Methods for Statistical Analysis of Covariance Structures - [electronic resource]](/Users/Baul/Images/book.png)
Novel Methods for Statistical Analysis of Covariance Structures- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214100110
- ISBN
- 9798379750831
- DDC
- 574
- 저자명
- Chen, Andrew A.
- 서명/저자
- Novel Methods for Statistical Analysis of Covariance Structures - [electronic resource]
- 발행사항
- [S.l.]: : University of Pennsylvania., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(140 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
- 주기사항
- Advisor: Shinohara, Russell T.;Shou, Haochang.
- 학위논문주기
- Thesis (Ph.D.)--University of Pennsylvania, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약Neuroscientists increasingly understand brain development and pathology through relationships between complex measurements. These measurements include neuroimaging, genetic, and mobile health data that contain distinct but complementary information. However, the associations between these data and outcomes of interest can be difficult to detect and often require samples acquired across multiple study centers. This multi-site design can introduce bias in the form of site effects, which have been demonstrated to severely impact downstream analyses. Here, we develop methods for analysis of covariance structures in structural imaging, functional imaging, and mobile health studies. In structural imaging, we find that site effects in covariance can bias machine learning results and propose methodology for mitigating this bias. In functional imaging, we discover that site effects in subject-specific covariance structures can impact downstream network analyses and we develop several methods for addressing these effects. We additionally develop a multimodal regression framework that leverages the covariance among data modalities, which we apply in imaging and mobile health studies. We evaluate the performance and utility of our methodologies through simulations and applications to several notable multi-site and multimodal studies.
- 일반주제명
- Biostatistics.
- 일반주제명
- Neurosciences.
- 일반주제명
- Medical imaging.
- 일반주제명
- Bioinformatics.
- 키워드
- Batch effects
- 키워드
- Covariance
- 키워드
- Multimodal data
- 키워드
- Neuroimaging
- 기타저자
- University of Pennsylvania Epidemiology and Biostatistics
- 기본자료저록
- Dissertations Abstracts International. 84-12B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008240612s2023 us |||||||||||||||c||eng d■001000016931730
■00520240214100110
■006m o d
■007cr#unu||||||||
■020 ▼a9798379750831
■035 ▼a(MiAaPQ)AAI30420532
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a574
■1001 ▼aChen, Andrew A.
■24510▼aNovel Methods for Statistical Analysis of Covariance Structures▼h[electronic resource]
■260 ▼a[S.l.]:▼bUniversity of Pennsylvania. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(140 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 84-12, Section: B.
■500 ▼aAdvisor: Shinohara, Russell T.;Shou, Haochang.
■5021 ▼aThesis (Ph.D.)--University of Pennsylvania, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aNeuroscientists increasingly understand brain development and pathology through relationships between complex measurements. These measurements include neuroimaging, genetic, and mobile health data that contain distinct but complementary information. However, the associations between these data and outcomes of interest can be difficult to detect and often require samples acquired across multiple study centers. This multi-site design can introduce bias in the form of site effects, which have been demonstrated to severely impact downstream analyses. Here, we develop methods for analysis of covariance structures in structural imaging, functional imaging, and mobile health studies. In structural imaging, we find that site effects in covariance can bias machine learning results and propose methodology for mitigating this bias. In functional imaging, we discover that site effects in subject-specific covariance structures can impact downstream network analyses and we develop several methods for addressing these effects. We additionally develop a multimodal regression framework that leverages the covariance among data modalities, which we apply in imaging and mobile health studies. We evaluate the performance and utility of our methodologies through simulations and applications to several notable multi-site and multimodal studies.
■590 ▼aSchool code: 0175.
■650 4▼aBiostatistics.
■650 4▼aNeurosciences.
■650 4▼aMedical imaging.
■650 4▼aBioinformatics.
■653 ▼aBatch effects
■653 ▼aCovariance
■653 ▼aFunctional imaging
■653 ▼aMultimodal data
■653 ▼aNeuroimaging
■653 ▼aStructural imaging
■690 ▼a0308
■690 ▼a0574
■690 ▼a0317
■690 ▼a0715
■71020▼aUniversity of Pennsylvania▼bEpidemiology and Biostatistics.
■7730 ▼tDissertations Abstracts International▼g84-12B.
■773 ▼tDissertation Abstract International
■790 ▼a0175
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16931730▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
