서브메뉴
검색
Quickest Change Detection Under Post-Change Non-Stationarity and Uncertainty
Quickest Change Detection Under Post-Change Non-Stationarity and Uncertainty
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260209102848
- ISBN
- 9798291563755
- DDC
- 310
- 저자명
- Liang, Yuchen.
- 서명/저자
- Quickest Change Detection Under Post-Change Non-Stationarity and Uncertainty
- 발행사항
- [Sl] : University of Illinois at Urbana-Champaign, 2023
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2023
- 형태사항
- 149 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-02, Section: B.
- 주기사항
- Advisor: Veeravalli, Venugopal V.
- 학위논문주기
- Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2023.
- 초록/해제
- 요약The problem of quickest change detection in a sequence of independent observations is considered. Given sequential observations, the problem aims to detect the change after it occurs as quickly as possible, subject to false alarm constraints. The goal of this dissertation is to extend classical theories of quickest change detection for cases where the post-change observations are non-stationary, and/or where the post-change distribution is not fully known nor belongs to known parametric families.The first problem considered is the mean-change detection problem, where a change in the mean of an observation sequence above some threshold is of interest. The post-change observations are allowed to be non-stationary, and no knowledge of the post-change distribution is assumed other than that its mean is above the threshold. The problem is formulated as a robust change detection problem, and the Mean-Change Test (MCT) is derived, which is shown to be asymptotically close to the minimax robust solution.In the second problem, the post-change observations are assumed non-stationary with possible parametric uncertainty in their distribution, where this non-stationarity is characterized by the cumulative Kullback-Leibler divergence between the post- and the pre-change distributions. A universal asymptotic lower bound on the delay is derived. For the case where the post-change distributions have parametric uncertainty, a window-limited (WL) generalized likelihood-ratio (GLR) CuSum test is developed which is shown to be asymptotically optimal. The use of the WL-GLR-CuSum test in monitoring pandemics is also demonstrated.The next two problems focus on observation models where there is a lack of concrete knowledge of the post-change distribution. In the third problem, it is assumed that the only information about the post-change distribution is through a (small) set of training data. The problem is formulated as a data-driven robust change detection problem, where the post-change uncertainty set is constructed using the Wasserstein distance from the empirical distribution. The distributional robust (DR) CuSum test is constructed and is shown to be asymptotically minimax robust. The size of the uncertainty set is theoretically characterized using Wasserstein concentration bounds.In the last problem, it is assumed that the post-change distribution is completely unknown. Two tests, the window-limited non-parametric generalized likelihood ratio (NGLR) CuSum test and the non-parametric window-limited adaptive (NWLA) CuSum test, are developed with generic density estimators. Both tests do not require any pre-collected training samples. The tests are shown to achieve first-order asymptotic optimality under certain convergence conditions on the density estimator.
- 일반주제명
- Statistics
- 일반주제명
- Electrical engineering
- 일반주제명
- Computer engineering
- 기타저자
- University of Illinois at Urbana-Champaign Electrical & Computer Eng
- 기본자료저록
- Dissertations Abstracts International. 87-02B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008260203s2023 us c eng d■001000017365888
■00520260209102848
■006m o d
■007cr#unu||||||||
■020 ▼a9798291563755
■035 ▼a(MiAaPQ)AAI32271352
■035 ▼a(MiAaPQ)httphdlhandlenet2142121436
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a310
■1001 ▼aLiang, Yuchen.
■24510▼aQuickest Change Detection Under Post-Change Non-Stationarity and Uncertainty
■260 ▼a[Sl]▼bUniversity of Illinois at Urbana-Champaign▼c2023
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2023
■300 ▼a149 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-02, Section: B.
■500 ▼aAdvisor: Veeravalli, Venugopal V.
■5021 ▼aThesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2023.
■520 ▼aThe problem of quickest change detection in a sequence of independent observations is considered. Given sequential observations, the problem aims to detect the change after it occurs as quickly as possible, subject to false alarm constraints. The goal of this dissertation is to extend classical theories of quickest change detection for cases where the post-change observations are non-stationary, and/or where the post-change distribution is not fully known nor belongs to known parametric families.The first problem considered is the mean-change detection problem, where a change in the mean of an observation sequence above some threshold is of interest. The post-change observations are allowed to be non-stationary, and no knowledge of the post-change distribution is assumed other than that its mean is above the threshold. The problem is formulated as a robust change detection problem, and the Mean-Change Test (MCT) is derived, which is shown to be asymptotically close to the minimax robust solution.In the second problem, the post-change observations are assumed non-stationary with possible parametric uncertainty in their distribution, where this non-stationarity is characterized by the cumulative Kullback-Leibler divergence between the post- and the pre-change distributions. A universal asymptotic lower bound on the delay is derived. For the case where the post-change distributions have parametric uncertainty, a window-limited (WL) generalized likelihood-ratio (GLR) CuSum test is developed which is shown to be asymptotically optimal. The use of the WL-GLR-CuSum test in monitoring pandemics is also demonstrated.The next two problems focus on observation models where there is a lack of concrete knowledge of the post-change distribution. In the third problem, it is assumed that the only information about the post-change distribution is through a (small) set of training data. The problem is formulated as a data-driven robust change detection problem, where the post-change uncertainty set is constructed using the Wasserstein distance from the empirical distribution. The distributional robust (DR) CuSum test is constructed and is shown to be asymptotically minimax robust. The size of the uncertainty set is theoretically characterized using Wasserstein concentration bounds.In the last problem, it is assumed that the post-change distribution is completely unknown. Two tests, the window-limited non-parametric generalized likelihood ratio (NGLR) CuSum test and the non-parametric window-limited adaptive (NWLA) CuSum test, are developed with generic density estimators. Both tests do not require any pre-collected training samples. The tests are shown to achieve first-order asymptotic optimality under certain convergence conditions on the density estimator.
■590 ▼aSchool code: 0090.
■650 4▼aStatistics
■650 4▼aElectrical engineering
■650 4▼aComputer engineering
■653 ▼aQuickest change detection
■653 ▼aNon-stationary observations
■653 ▼aNon-parametric methods
■653 ▼aDensity estimation
■653 ▼aOptimal robust detection
■653 ▼aWasserstein distance
■690 ▼a0544
■690 ▼a0463
■690 ▼a0464
■71020▼aUniversity of Illinois at Urbana-Champaign▼bElectrical & Computer Eng.
■7730 ▼tDissertations Abstracts International▼g87-02B.
■790 ▼a0090
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17365888▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
