1
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Xu Q, Mei Y. Asymptotic Optimality Theory for Active Quickest Detection with Unknown PostChange Parameters. Seq Anal 2023; 42:150-181. [PMID: 37645693 PMCID: PMC10462384 DOI: 10.1080/07474946.2023.2187417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 02/02/2023] [Accepted: 02/21/2023] [Indexed: 08/31/2023]
Abstract
The active quickest detection problem with unknown post-change parameters is studied under the sampling control constraint, where there are p local streams in a system but one is only able to take observations from one and only one of these p local streams at each time instant. The objective is to raise a correct alarm as quickly as possible once the change occurs subject to both false alarm and sampling control constraints. Here we assume that exactly one of the p local streams is affected, and the post-change distribution involves unknown parameters. In this context, we propose an efficient greedy-cyclic-sampling-based quickest detection algorithm, and show that our proposed algorithm is asymptotically optimal in the sense of minimizing the detection delay under both false alarm and sampling control constraints. Numerical studies are conducted to show the effectiveness and applicability of the proposed algorithm.
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Affiliation(s)
- Qunzhi Xu
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Yajun Mei
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
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2
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Cui J, Wang G, Zou C, Wang Z. Change-point testing for parallel data sets with FDR control. Comput Stat Data Anal 2023. [DOI: 10.1016/j.csda.2023.107705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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3
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Zhao X, Hu J, Mei Y, Yan H. Adaptive Partially Observed Sequential Change Detection and Isolation. Technometrics 2022; 64:502-512. [PMID: 37388823 PMCID: PMC10310291 DOI: 10.1080/00401706.2022.2124307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Revised: 08/20/2022] [Accepted: 08/20/2022] [Indexed: 10/14/2022]
Abstract
High-dimensional data has become popular due to the easy accessibility of sensors in modern industrial applications. However, one specific challenge is that it is often not easy to obtain complete measurements due to limited sensing powers and resource constraints. Furthermore, distinct failure patterns may exist in the systems, and it is necessary to identify the true failure pattern. This work focuses on the online adaptive monitoring of high-dimensional data in resource-constrained environments with multiple potential failure modes. To achieve this, we propose to apply the Shiryaev-Roberts procedure on the failure mode level and utilize the multi-arm bandit to balance the exploration and exploitation. We further discuss the theoretical property of the proposed algorithm to show that the proposed method can correctly isolate the failure mode. Finally, extensive simulations and two case studies demonstrate that the change point detection performance and the failure mode isolation accuracy can be greatly improved.
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Affiliation(s)
- Xinyu Zhao
- School of Computing and Augmented Intelligence, Arizona State University
| | - Jiuyun Hu
- School of Computing and Augmented Intelligence, Arizona State University
| | - Yajun Mei
- School of Industrial and Systems Engineering, Georgia Institute of Technology
| | - Hao Yan
- School of Computing and Augmented Intelligence, Arizona State University
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4
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Zan X, Wang D, Xian X. Spatial Rank-Based Augmentation for Nonparametric Online Monitoring and Adaptive Sampling of Big Data Streams. Technometrics 2022. [DOI: 10.1080/00401706.2022.2143903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Xin Zan
- Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL 32611
| | - Di Wang
- Department of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaochen Xian
- Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL 32611
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5
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Mozaffari M, Doshi K, Yilmaz Y. Online Multivariate Anomaly Detection and Localization for High-Dimensional Settings. SENSORS (BASEL, SWITZERLAND) 2022; 22:8264. [PMID: 36365962 PMCID: PMC9656001 DOI: 10.3390/s22218264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 10/21/2022] [Accepted: 10/24/2022] [Indexed: 06/16/2023]
Abstract
This paper considers the real-time detection of abrupt and persistent anomalies in high-dimensional data streams. The goal is to detect anomalies quickly and accurately so that the appropriate countermeasures could be taken in time before the system possibly gets harmed. We propose a sequential and multivariate anomaly detection method that scales well to high-dimensional datasets. The proposed method follows a nonparametric, i.e., data-driven, and semi-supervised approach, i.e., trains only on nominal data. Thus, it is applicable to a wide range of applications and data types. Thanks to its multivariate nature, it can quickly and accurately detect challenging anomalies, such as changes in the correlation structure. Its asymptotic optimality and computational complexity are comprehensively analyzed. In conjunction with the detection method, an effective technique for localizing the anomalous data dimensions is also proposed. The practical use of proposed algorithms are demonstrated using synthetic and real data, and in variety of applications including seizure detection, DDoS attack detection, and video surveillance.
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6
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Haydari A, Yilmaz Y. RSU-Based Online Intrusion Detection and Mitigation for VANET. SENSORS (BASEL, SWITZERLAND) 2022; 22:7612. [PMID: 36236712 PMCID: PMC9573621 DOI: 10.3390/s22197612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 09/30/2022] [Accepted: 10/05/2022] [Indexed: 06/16/2023]
Abstract
Secure vehicular communication is a critical factor for secure traffic management. Effective security in intelligent transportation systems (ITS) requires effective and timely intrusion detection systems (IDS). In this paper, we consider false data injection attacks and distributed denial-of-service (DDoS) attacks, especially the stealthy DDoS attacks, targeting integrity and availability, respectively, in vehicular ad-hoc networks (VANET). Novel machine learning techniques for intrusion detection and mitigation based on centralized communications through roadside units (RSU) are proposed for the considered attacks. The performance of the proposed methods is evaluated using a traffic simulator and a real traffic dataset. Comparisons with the state-of-the-art solutions clearly demonstrate the superior detection and localization performance of the proposed methods by 78% in the best case and 27% in the worst case, while achieving the same level of false alarm probability.
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7
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Guo J, Yan H, Zhang C. A Bayesian Partially Observable Online Change Detection Approach with Thompson Sampling. Technometrics 2022. [DOI: 10.1080/00401706.2022.2127914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Affiliation(s)
- Jie Guo
- Tsinghua University, Tsinghua University, Beijing, 100084 China
| | - Hao Yan
- Arizona State University, Tempe, United States
| | - Chen Zhang
- Tsinghua University, Tsinghua University, Beijing, 100084 China
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8
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Hu J, Mei Y, Holte S, Yan H. Adaptive resources allocation CUSUM for binomial count data monitoring with application to COVID-19 hotspot detection. J Appl Stat 2022; 50:2889-2913. [PMID: 37808611 PMCID: PMC10557554 DOI: 10.1080/02664763.2022.2117288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 08/16/2022] [Indexed: 10/14/2022]
Abstract
In this paper, we present an efficient statistical method (denoted as 'Adaptive Resources Allocation CUSUM') to robustly and efficiently detect the hotspot with limited sampling resources. Our main idea is to combine the multi-arm bandit (MAB) and change-point detection methods to balance the exploration and exploitation of resource allocation for hotspot detection. Further, a Bayesian weighted update is used to update the posterior distribution of the infection rate. Then, the upper confidence bound (UCB) is used for resource allocation and planning. Finally, CUSUM monitoring statistics to detect the change point as well as the change location. For performance evaluation, we compare the performance of the proposed method with several benchmark methods in the literature and showed the proposed algorithm is able to achieve a lower detection delay and higher detection precision. Finally, this method is applied to hotspot detection in a real case study of county-level daily positive COVID-19 cases in Washington State WA) and demonstrates the effectiveness with very limited distributed samples.
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Affiliation(s)
- Jiuyun Hu
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
| | - Yajun Mei
- School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Sarah Holte
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Hao Yan
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
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9
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Follain B, Wang T, Samworth RJ. High‐dimensional changepoint estimation with heterogeneous missingness. J R Stat Soc Series B Stat Methodol 2022. [DOI: 10.1111/rssb.12540] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Bertille Follain
- Statistical LaboratoryUniversity of Cambridge CambridgeCambridgeshireUK
- Ecole Normale SupérieurePSL Research University, INRIA ParisFrance
| | - Tengyao Wang
- Department of StatisticsLondon School of Economics and Political Science LondonLondonUK
- Department of Statistical ScienceUniversity College London LondonUK
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10
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Wu Y, Wu WB. Sequential common rate decrease detection, isolation, and estimation in multiple Poisson processes. J STAT COMPUT SIM 2022. [DOI: 10.1080/00949655.2022.2084617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Yanhong Wu
- Department of Mathematics, California State University Stanislaus, Turlock, CA, USA
| | - Wei Biao Wu
- Department of Statistics, University of Chicago, Chicago, IL, USA
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11
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Zhang R, Mei Y, Shi J. Robust change detection for large-scale data streams. Seq Anal 2022. [DOI: 10.1080/07474946.2022.2043045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Ruizhi Zhang
- Department of Statistics, University of Nebraska–Lincoln, Lincoln, Nebraska, USA
| | - Yajun Mei
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Jianjun Shi
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
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12
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Zhang W, Mei Y. Bandit Change-Point Detection for Real-Time Monitoring High-Dimensional Data Under Sampling Control. Technometrics 2022; 65:33-43. [PMID: 36950530 PMCID: PMC10027391 DOI: 10.1080/00401706.2022.2054861] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 03/09/2022] [Accepted: 03/12/2022] [Indexed: 10/18/2022]
Abstract
In many real-world problems of real-time monitoring high-dimensional streaming data, one wants to detect an undesired event or change quickly once it occurs, but under the sampling control constraint in the sense that one might be able to only observe or use selected components data for decision-making per time step in the resource-constrained environments. In this paper, we propose to incorporate multi-armed bandit approaches into sequential change-point detection to develop an efficient bandit change-point detection algorithm based on the limiting Bayesian approach to incorporate a prior knowledge of potential changes. Our proposed algorithm, termed Thompson-Sampling-Shiryaev-Roberts-Pollak (TSSRP), consists of two policies per time step: the adaptive sampling policy applies the Thompson Sampling algorithm to balance between exploration for acquiring long-term knowledge and exploitation for immediate reward gain, and the statistical decision policy fuses the local Shiryaev-Roberts-Pollak statistics to determine whether to raise a global alarm by sum shrinkage techniques. Extensive numerical simulations and case studies demonstrate the statistical and computational efficiency of our proposed TSSRP algorithm.
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13
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Wu Y, Wu WB. Sequential common change detection, isolation, and estimation in multiple poisson processes. Seq Anal 2022. [DOI: 10.1080/07474946.2022.2043054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Yanhong Wu
- Department of Mathematics, California State University Stanislaus, Turlock, California, USA
| | - Wei Biao Wu
- Department of Statistics, University of Chicago, Chicago, Illinois, USA
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14
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Hahn G. Online multivariate changepoint detection with type I error control and constant time/memory updates per series. Stat Probab Lett 2022. [DOI: 10.1016/j.spl.2021.109258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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15
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Chen Y, Wang T, Samworth RJ. High‐dimensional, multiscale online changepoint detection. J R Stat Soc Series B Stat Methodol 2022. [DOI: 10.1111/rssb.12447] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Yudong Chen
- University of Cambridge Cambridge Cambridgeshire UK
| | - Tengyao Wang
- London School of Economics and Political Science London UK
- University College London London UK
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16
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Gösmann J, Stoehr C, Heiny J, Dette H. Sequential change point detection in high dimensional time series. Electron J Stat 2022. [DOI: 10.1214/22-ejs2027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
| | | | | | - Holger Dette
- Department of Mathematics, Ruhr University Bochum
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17
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Fang Z, Li W, Liu X, Pu X, Xiang D. Online monitoring of high-dimensional binary data streams with application to extreme weather surveillance. J Appl Stat 2021; 49:4122-4136. [DOI: 10.1080/02664763.2021.1971633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Zhiwen Fang
- KLATASDS-MOE, School of Statistics, East China Normal University, Shanghai, People's Republic of China
| | - Wendong Li
- School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, People's Republic of China
| | - Xin Liu
- School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, People's Republic of China
| | - Xiaolong Pu
- KLATASDS-MOE, School of Statistics, East China Normal University, Shanghai, People's Republic of China
| | - Dongdong Xiang
- KLATASDS-MOE, School of Statistics, East China Normal University, Shanghai, People's Republic of China
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18
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Pollak M. A rule of thumb: Run lengths to false alarm of many types of control charts run in parallel on dependent streams are asymptotically independent. Ann Stat 2021. [DOI: 10.1214/20-aos1968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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19
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Nath S, Wu J. Quickest change point detection with multiple postchange models. Seq Anal 2021. [DOI: 10.1080/07474946.2020.1826795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Samrat Nath
- Department of Electrical Engineering, University of Arkansas, Fayetteville, Arkansas, USA
| | - Jingxian Wu
- Department of Electrical Engineering, University of Arkansas, Fayetteville, Arkansas, USA
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20
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Common Change-Point Estimation and Changed Panel Isolation after Sequential Detection in an Exponential Family. JOURNAL OF STATISTICAL THEORY AND PRACTICE 2020. [DOI: 10.1007/s42519-020-00134-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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21
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Ren H, Zou C, Chen N, Li R. Large-Scale Datastreams Surveillance via Pattern-Oriented-Sampling. J Am Stat Assoc 2020. [DOI: 10.1080/01621459.2020.1819295] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Haojie Ren
- School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China
- Department of Statistics, The Pennsylvania State University at University Park, State College, PA
| | - Changliang Zou
- School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin, China
| | - Nan Chen
- Department of Industrial Systems Engineering and Management, National University of Singapore, Singapore
| | - Runze Li
- Department of Statistics, The Pennsylvania State University at University Park, State College, PA
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22
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Guo L, Modarres R. Two multivariate online change detection models. J Appl Stat 2020; 49:427-448. [PMID: 35707208 PMCID: PMC9196088 DOI: 10.1080/02664763.2020.1815674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Accepted: 08/22/2020] [Indexed: 10/23/2022]
Abstract
Online change point detection methods monitor changes in the distribution of a data stream. This article discusses two non-parametric online change detection methods based on the energy statistics and Mahalanobis depth. To apply the energy statistic, we use sliding-window algorithm with efficient training and updating procedures. For Mahalanobis depth, we propose an algorithm to train the threshold with desired protective ability against false alarms and discuss factors that have an influence on the threshold. Numerical studies evaluate and compare the performance of the proposed models with three existing methods to detect changes in the mean and variability of a data stream. The methods are applied to detecting changes in the flowing volume of the Mississippi River.
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Affiliation(s)
- Lingzhe Guo
- Department of Statistics, The George Washington University, Washington, DC, USA
| | - Reza Modarres
- Department of Statistics, The George Washington University, Washington, DC, USA
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23
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Eckley I, Kirch C, Weber S. A novel change-point approach for the detection of gas emission sources using remotely contained concentration data. Ann Appl Stat 2020. [DOI: 10.1214/20-aoas1345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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24
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Wu Y. Isolating changed panels and estimating common change point after sequential detection with FDR control. COMMUN STAT-SIMUL C 2020. [DOI: 10.1080/03610918.2020.1795194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Yanhong Wu
- Department of Mathematics, California State University Stanislaus, Turlock, California, USA
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25
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Affiliation(s)
- Georgios Rovatsos
- Department of Electrical and Computer Engineering and Coordinated Science Laboratory, University of Illinois at Urbana–Champaign, Urbana, Illinois, USA
| | - Shaofeng Zou
- Department of Electrical Engineering, University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - Venugopal V. Veeravalli
- Department of Electrical and Computer Engineering and Coordinated Science Laboratory, University of Illinois at Urbana–Champaign, Urbana, Illinois, USA
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26
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Wu Y. Estimation of common change point and isolation of changed panels after sequential detection. Seq Anal 2020. [DOI: 10.1080/07474946.2020.1726685] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Yanhong Wu
- Department of Mathematics, California State University Stanislaus, Turlock, California, USA
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27
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Hubig L, Lack N, Mansmann U. Statistical process monitoring to improve quality assurance of inpatient care. BMC Health Serv Res 2020; 20:21. [PMID: 31910826 PMCID: PMC6947979 DOI: 10.1186/s12913-019-4866-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Accepted: 12/23/2019] [Indexed: 11/14/2022] Open
Abstract
Background Statistical Process Monitoring (SPM) is not typically used in traditional quality assurance of inpatient care. While SPM allows a rapid detection of performance deficits, SPM results strongly depend on characteristics of the evaluated process. When using SPM to monitor inpatient care, in particular the hospital risk profile, hospital volume and properties of each monitored performance indicator (e.g. baseline failure probability) influence the results and must be taken into account to ensure a fair process evaluation. Here we study the use of CUSUM charts constructed for a predefined false alarm probability within a single process, i.e. a given hospital and performance indicator. We furthermore assess different monitoring schemes based on the resulting CUSUM chart and their dependence on the process characteristics. Methods We conduct simulation studies in order to investigate alarm characteristics of the Bernoulli log-likelihood CUSUM chart for crude and risk-adjusted performance indicators, and illustrate CUSUM charts on performance data from the external quality assurance of hospitals in Bavaria, Germany. Results Simulating CUSUM control limits for a false alarm probability allows to control the number of false alarms across different conditions and monitoring schemes. We gained better understanding of the effect of different factors on the alarm rates of CUSUM charts. We propose using simulations to assess the performance of implemented CUSUM charts. Conclusions The presented results and example demonstrate the application of CUSUM charts for fair performance evaluation of inpatient care. We propose the simulation of CUSUM control limits while taking into account hospital and process characteristics.
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Affiliation(s)
- Lena Hubig
- Institute for Medical Information Processing, Biometry, and Epidemiology, Ludwig-Maximilians-Universität, Marchioninistr. 15, Munich, 81377, Germany.
| | - Nicholas Lack
- Bavarian Institute for Quality Assurance, Munich, 80331, Germany
| | - Ulrich Mansmann
- Institute for Medical Information Processing, Biometry, and Epidemiology, Ludwig-Maximilians-Universität, Marchioninistr. 15, Munich, 81377, Germany
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28
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Hormozinejad F. Optimal tests for arbitrarily varying object and the familywise error rates. COMMUN STAT-SIMUL C 2019. [DOI: 10.1080/03610918.2019.1661472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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29
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Tartakovsky AG. Asymptotically Optimal Quickest Change Detection in Multistream Data—Part 1: General Stochastic Models. Methodol Comput Appl Probab 2019. [DOI: 10.1007/s11009-019-09735-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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30
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31
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Li W, Xiang D, Tsung F, Pu X. A Diagnostic Procedure for High-Dimensional Data Streams via Missed Discovery Rate Control. Technometrics 2019. [DOI: 10.1080/00401706.2019.1575284] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Wendong Li
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, China
| | - Dongdong Xiang
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, China
| | - Fugee Tsung
- Department of Industrial Engineering and Decision Analytics, Hong Kong University of Science and Technology, Kowloon, Hong Kong
| | - Xiaolong Pu
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, China
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32
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Zhang R, Mei Y. Asymptotic statistical properties of communication-efficient quickest detection schemes in sensor networks. Seq Anal 2019. [DOI: 10.1080/07474946.2018.1548849] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Ruizhi Zhang
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Yajun Mei
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
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33
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Affiliation(s)
- Yanhong Wu
- Department of Mathematics, California State University Stanislaus, Turlock, California, USA
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34
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Madrid Padilla OH, Athey A, Reinhart A, Scott JG. Sequential Nonparametric Tests for a Change in Distribution: An Application to Detecting Radiological Anomalies. J Am Stat Assoc 2018. [DOI: 10.1080/01621459.2018.1476245] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Affiliation(s)
| | - Alex Athey
- Applied Research Laboratories, University of Texas at Austin, Austin, TX
| | - Alex Reinhart
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA
| | - James G. Scott
- Department of Statistics and Data Sciences and McCombs School of Business, University of Texas at Austin, Austin, TX
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35
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Yan H, Paynabar K, Shi J. Real-Time Monitoring of High-Dimensional Functional Data Streams via Spatio-Temporal Smooth Sparse Decomposition. Technometrics 2018. [DOI: 10.1080/00401706.2017.1346522] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Hao Yan
- Georgia Institute of Technology, Atlanta, GA
| | | | - Jianjun Shi
- Georgia Institute of Technology, Atlanta, GA
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36
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Deng L, Zi X, Li Z. False discovery rates for large-scale model checking under certain dependence. COMMUN STAT-THEOR M 2018. [DOI: 10.1080/03610926.2017.1300279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Lu Deng
- Institute of Statistics and LPMC, Nankai University, Tianjin, P. R. China
| | - Xuemin Zi
- School of Science, Tianjin University of Technology and Education, Tianjin, P. R. China
| | - Zhonghua Li
- Institute of Statistics and LPMC, Nankai University, Tianjin, P. R. China
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Xian X, Wang A, Liu K. A Nonparametric Adaptive Sampling Strategy for Online Monitoring of Big Data Streams. Technometrics 2017. [DOI: 10.1080/00401706.2017.1317291] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Xiaochen Xian
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI
| | - Andi Wang
- Department of Industrial Engineering and Logistics Management, Hong Kong University of Science and Technology, Clear Waterbay, NT, Hong Kong
| | - Kaibo Liu
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI
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Lai TL, Tsang KW. Discussion on “Sequential detection/isolation of abrupt changes” by Igor V. Nikiforov. Seq Anal 2016. [DOI: 10.1080/07474946.2016.1206372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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41
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Tartakovsky AG. Discussion on “Sequential detection/isolation of abrupt changes” by Igor V. Nikiforov. Seq Anal 2016. [DOI: 10.1080/07474946.2016.1206375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Bartroff J, Song J. A Rejection Principle for Sequential Tests of Multiple Hypotheses Controlling Familywise Error Rates. Scand Stat Theory Appl 2015; 31:3-19. [PMID: 26985125 DOI: 10.1111/sjos.12161] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
We present a unifying approach to multiple testing procedures for sequential (or streaming) data by giving sufficient conditions for a sequential multiple testing procedure to control the familywise error rate (FWER). Together we call these conditions a "rejection principle for sequential tests," which we then apply to some existing sequential multiple testing procedures to give simplified understanding of their FWER control. Next the principle is applied to derive two new sequential multiple testing procedures with provable FWER control, one for testing hypotheses in order and another for closed testing. Examples of these new procedures are given by applying them to a chromosome aberration data set and to finding the maximum safe dose of a treatment.
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Affiliation(s)
- Jay Bartroff
- Department of Mathematics, University of Southern California
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Bartroff J, Song J. Sequential Tests of Multiple Hypotheses Controlling Type I and II Familywise Error Rates. J Stat Plan Inference 2014; 153:100-114. [PMID: 25092948 PMCID: PMC4118217 DOI: 10.1016/j.jspi.2014.05.010] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
This paper addresses the following general scenario: A scientist wishes to perform a battery of experiments, each generating a sequential stream of data, to investigate some phenomenon. The scientist would like to control the overall error rate in order to draw statistically-valid conclusions from each experiment, while being as efficient as possible. The between-stream data may differ in distribution and dimension but also may be highly correlated, even duplicated exactly in some cases. Treating each experiment as a hypothesis test and adopting the familywise error rate (FWER) metric, we give a procedure that sequentially tests each hypothesis while controlling both the type I and II FWERs regardless of the between-stream correlation, and only requires arbitrary sequential test statistics that control the error rates for a given stream in isolation. The proposed procedure, which we call the sequential Holm procedure because of its inspiration from Holm's (1979) seminal fixed-sample procedure, shows simultaneous savings in expected sample size and less conservative error control relative to fixed sample, sequential Bonferroni, and other recently proposed sequential procedures in a simulation study.
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Affiliation(s)
- Jay Bartroff
- Department of Mathematics, University of Southern California, Los Angeles, California, USA
| | - Jinlin Song
- Analysis Group, 111 Huntington Avenue, Tenth Floor, Boston, MA 02199, USA
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Liu K, Mei Y, Shi J. An Adaptive Sampling Strategy for Online High-Dimensional Process Monitoring. Technometrics 2014. [DOI: 10.1080/00401706.2014.947005] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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47
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Zou C, Wang Z, Zi X, Jiang W. An Efficient Online Monitoring Method for High-Dimensional Data Streams. Technometrics 2014. [DOI: 10.1080/00401706.2014.940089] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Han SW, Jiang W, Shu L, Tsui KL. A Comparison of Likelihood-Based Spatiotemporal Monitoring Methods Under Non-Homogenous Population Size. COMMUN STAT-SIMUL C 2014. [DOI: 10.1080/03610918.2013.763981] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Han SW, Lee KJ, Zhong H, Kim SB. A Comparison of Spatiotemporal Surveillance Methods for Nonhomogeneous Change Size. COMMUN STAT-SIMUL C 2014. [DOI: 10.1080/03610918.2013.844837] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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