서브메뉴
검색
Uncertainty and Risk Quantification in High-Dimensional Statistics: Methods for Non-Traditional Settings
Uncertainty and Risk Quantification in High-Dimensional Statistics: Methods for Non-Traditional Settings
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103505
- ISBN
- 9798280719040
- DDC
- 310
- 저자명
- Jiang, Kuanhao.
- 서명/저자
- Uncertainty and Risk Quantification in High-Dimensional Statistics: Methods for Non-Traditional Settings
- 발행사항
- [Sl] : Harvard University, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 340 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
- 주기사항
- Advisor: Sur, Pragya.
- 학위논문주기
- Thesis (Ph.D.)--Harvard University, 2025.
- 초록/해제
- 요약High-dimensional data are increasingly common across fields such as genomics, economics, and neuroscience, often challenging conventional statistical methods. This dissertation develops new tools for uncertainty quantification in high-dimensional settings where standard assumptions-like sparsity or data homogeneity-may not apply. The first part focuses on high-dimensional causal inference without sparsity. We analyze cross-fitted estimators and derive the asymptotic distribution of the cross-fitted augmented inverse probability weighting (AIPW) estimator under a proportional asymptotics regime. Our results highlight how cross-fitting and regularization impact estimation risk, enabling more accurate inference even in dense, high-dimensional designs. The second part addresses predictive inference under distributional heterogeneity. Classical conformal methods assume identically distributed data, an assumption violated in many real-world applications. We propose conformal algorithms for multi-environment settings, offering valid prediction intervals under minimal assumptions. These methods apply to both regression and classification, support general loss functions, and can incorporate auxiliary information to reduce interval size without compromising coverage. Together, these results advance uncertainty quantification in modern, complex data environments.
- 일반주제명
- Statistics
- 일반주제명
- Statistical physics
- 일반주제명
- Computer science
- 기타저자
- Harvard University Statistics
- 기본자료저록
- Dissertations Abstracts International. 86-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008260126s2025 us c eng d■001000017357388
■00520260202103505
■006m o d
■007cr#unu||||||||
■020 ▼a9798280719040
■035 ▼a(MiAaPQ)AAI32002503
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a310
■1001 ▼aJiang, Kuanhao.▼0(orcid)0009-0004-9161-8507
■24510▼aUncertainty and Risk Quantification in High-Dimensional Statistics: Methods for Non-Traditional Settings
■260 ▼a[Sl]▼bHarvard University▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a340 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-12, Section: B.
■500 ▼aAdvisor: Sur, Pragya.
■5021 ▼aThesis (Ph.D.)--Harvard University, 2025.
■520 ▼aHigh-dimensional data are increasingly common across fields such as genomics, economics, and neuroscience, often challenging conventional statistical methods. This dissertation develops new tools for uncertainty quantification in high-dimensional settings where standard assumptions-like sparsity or data homogeneity-may not apply. The first part focuses on high-dimensional causal inference without sparsity. We analyze cross-fitted estimators and derive the asymptotic distribution of the cross-fitted augmented inverse probability weighting (AIPW) estimator under a proportional asymptotics regime. Our results highlight how cross-fitting and regularization impact estimation risk, enabling more accurate inference even in dense, high-dimensional designs. The second part addresses predictive inference under distributional heterogeneity. Classical conformal methods assume identically distributed data, an assumption violated in many real-world applications. We propose conformal algorithms for multi-environment settings, offering valid prediction intervals under minimal assumptions. These methods apply to both regression and classification, support general loss functions, and can incorporate auxiliary information to reduce interval size without compromising coverage. Together, these results advance uncertainty quantification in modern, complex data environments.
■590 ▼aSchool code: 0084.
■650 4▼aStatistics
■650 4▼aStatistical physics
■650 4▼aComputer science
■653 ▼aConformal prediction
■653 ▼aCross-fitting estimators
■653 ▼aHierarchical sampling
■653 ▼aHigh-dimensional causal inference
■653 ▼aConformal algorithms
■690 ▼a0463
■690 ▼a0984
■690 ▼a0217
■71020▼aHarvard University▼bStatistics.
■7730 ▼tDissertations Abstracts International▼g86-12B.
■790 ▼a0084
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17357388▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
