A Statistical Control Chart forMonitoring High-dimensional Poisson Data Streams

被引:12
|
作者
Wang, Zhiyuan [1 ]
Li, Yanting [1 ]
Zhou, Xiaojun [1 ]
机构
[1] Shanghai Jiao Tong Univ, Dept Ind Engn & Management, Shanghai, Peoples R China
基金
中国国家自然科学基金;
关键词
multiple Poisson data; average run length; statistical process control; two-sided shift; goodness of fit; FALSE DISCOVERY RATE; COUNT DATA; SCHEMES;
D O I
10.1002/qre.2005
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Multivariate count data are popular in the quality monitoring of manufacturing and service industries. However, seldom effort has been paid on high-dimensional Poisson data and two-sided mean shift situation. In this article, a hybrid control chart for independent multivariate Poisson data is proposed. The new chart was constructed based on the test of goodness of fit, and the monitoring procedure of the chart was shown. The performance of the proposed chart was evaluated using Monte Carlo simulation. Numerical experiments show that the new chart is very powerful and sensitive at detecting both positive and negative mean shifts. Meanwhile, it is more robust than other existing multiple Poisson charts for both independent and correlated variables. Besides, a newstandardization method for Poisson datawas developed in this article. A real examplewas also shown to illustrate the detailed steps of the new chart. Copyright (C) 2016 JohnWiley & Sons, Ltd.
引用
收藏
页码:307 / 321
页数:15
相关论文
共 50 条
  • [21] Monitoring of high-dimensional and high-frequency data streams: A nonparametric approach
    Wang, Zhiqiong
    Li, Xin
    Wang, Ying
    Ma, Yanhui
    Xue, Li
    QUALITY TECHNOLOGY AND QUANTITATIVE MANAGEMENT, 2024,
  • [22] Dynamic Sparse Subspace Clustering for Evolving High-Dimensional Data Streams
    Sui, Jinping
    Liu, Zhen
    Liu, Li
    Jung, Alexander
    Li, Xiang
    IEEE TRANSACTIONS ON CYBERNETICS, 2022, 52 (06) : 4173 - 4186
  • [23] Efficient unsupervised drift detector for fast and high-dimensional data streams
    Vinicius M. A. Souza
    Antonio R. S. Parmezan
    Farhan A. Chowdhury
    Abdullah Mueen
    Knowledge and Information Systems, 2021, 63 : 1497 - 1527
  • [24] StreamSVC: A New Approach To Cluster Large And High-Dimensional Data Streams
    Saberi, Hasan
    Mehdiaghaei, Mohammadali
    WORLD CONGRESS ON ENGINEERING, WCE 2011, VOL III, 2011, : 1865 - 1870
  • [25] Anomaly detection in high-dimensional network data streams: A case study
    Zhang, Ji
    Gao, Qigang
    Wang, Hai
    ISI 2008: 2008 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENCE AND SECURITY INFORMATICS, 2008, : 251 - +
  • [26] A grid-based clustering algorithm for high-dimensional data streams
    Lu, YS
    Sun, YF
    Xu, GP
    Liu, G
    ADVANCED DATA MINING AND APPLICATIONS, PROCEEDINGS, 2005, 3584 : 824 - 831
  • [27] Monitoring and root-cause diagnostics of high-dimensional data streams
    Ebrahimi, Samaneh
    Ranjan, Chitta
    Paynabar, Kamran
    JOURNAL OF QUALITY TECHNOLOGY, 2022, 54 (01) : 20 - 43
  • [28] NGPCA: Clustering of high-dimensional and non-stationary data streams
    Migenda, Nico
    Moeller, Ralf
    Schenck, Wolfram
    SOFTWARE IMPACTS, 2024, 20
  • [29] HDG-Tree: A Structure for Clustering High-Dimensional Data Streams
    Ren, Jiadong
    Li, Lining
    Xia, Yan
    Ren, Jiadong
    2009 THIRD INTERNATIONAL SYMPOSIUM ON INTELLIGENT INFORMATION TECHNOLOGY APPLICATION, VOL 2, PROCEEDINGS, 2009, : 594 - +
  • [30] Efficient unsupervised drift detector for fast and high-dimensional data streams
    Souza, Vinicius M. A.
    Parmezan, Antonio R. S.
    Chowdhury, Farhan A.
    Mueen, Abdullah
    KNOWLEDGE AND INFORMATION SYSTEMS, 2021, 63 (06) : 1497 - 1527