A GLR control chart for monitoring a multinomial process

被引:13
|
作者
Lee, Jaeheon [1 ]
Peng, Yiming [2 ]
Wang, Ning [3 ]
Reynolds, Marion R., Jr. [4 ,5 ]
机构
[1] Chung Ang Univ, Dept Appl Stat, Seoul 156756, South Korea
[2] Genentech Inc, San Francisco, CA 94080 USA
[3] Bank Amer, Wilmington, DE 19884 USA
[4] Virginia Tech, Dept Stat, Blacksburg, VA 24061 USA
[5] Virginia Tech, Dept Forest Resources & Environm Conservat, Blacksburg, VA 24061 USA
关键词
ANOS; Bernoulli CUSUM chart; generalized likelihood ratio chart; multiple proportion; PROPORTIONS;
D O I
10.1002/qre.2143
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The problem of detecting changes in the parameter p in a Bernoulli process with two possible categories for each observation has been extensively investigated in the SPC literature, but there is much less work on detecting changes in the vector parameter p in a multinomial process where there are more than two categories. A few papers have considered the case in which the direction of the change in p is known, but there is almost no work for the important case in which the direction of the change is unknown and individual observations are obtained. This paper proposes a multinomial generalized likelihood ratio (MGLR) control chart based on a likelihood ratio statistic for monitoring p when individual observations are obtained and the direction and size of the change in p are unknown. A set of 2-sided Bernoulli cumulative sum (CUSUM) charts is proposed as a reasonable competitor of the MGLR chart. It is shown that the MGLR chart has better overall performance than the set of 2-sided Bernoulli CUSUM charts over a wide range of unknown shifts. Equations arc presented for obtaining the control limit of the MGLR chart when there arc three or four components in p.
引用
收藏
页码:1773 / 1782
页数:10
相关论文
共 50 条
  • [31] Control Chart for Monitoring Autocorrelated Process with Multiple Exogenous Inputs
    Poblador, Ma Sofia Criselda A.
    Barrios, Erniel B.
    COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2016, 45 (09) : 3373 - 3393
  • [32] A multivariate control chart for simultaneously monitoring process mean and variability
    Zhang, Jiujun
    Li, Zhonghua
    Wang, Zhaojun
    COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2010, 54 (10) : 2244 - 2252
  • [33] A synthetic control chart for monitoring process dispersion with sample range
    Chen, FL
    Huang, HJ
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2005, 26 (7-8): : 842 - 851
  • [34] Nonparametric Progressive Mean Control Chart for Monitoring Process Target
    Abbasi, Saddam Akber
    Miller, Arden
    Riaz, Muhammad
    QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, 2013, 29 (07) : 1069 - 1080
  • [35] A multivariate synthetic control chart for monitoring process mean vector
    Ghute, V. B.
    Shirke, D. T.
    COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 2008, 37 (13) : 2136 - 2148
  • [36] A New GWMA Control Chart for Monitoring Process Mean and Variability
    Huang, Chi-Jui
    COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 2015, 44 (18) : 3841 - 3856
  • [37] The quadruple moving average control chart for monitoring the process mean
    Alevizakos, Vasileios
    Chatterjee, Kashinath
    Koukouvinos, Christos
    COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 2023, 52 (09) : 2882 - 2916
  • [38] A multivariate adaptive control chart for simultaneously monitoring of the process parameters
    Sabahno, Hamed
    Khoo, Michael B. C.
    COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2024, 53 (04) : 2031 - 2049
  • [39] Process monitoring using inflated beta regression control chart
    Lima-Filho, Luiz M. A.
    Pereira, Tarciana Liberal
    Souza, Tatiene C.
    Bayer, Fabio M.
    PLOS ONE, 2020, 15 (07):
  • [40] A new, ewma control chart for monitoring the process standard deviation
    Castagliola, P
    6TH ISSAT INTERNATIONAL CONFERENCE ON RELIABILITY AND QUALITY IN DESIGN, PROCEEDINGS, 2000, : 233 - 237