A Binomial GLR Control Chart for Monitoring a Proportion

被引:26
|
作者
Huang, Wandi [1 ]
Reynolds, Marion R., Jr. [2 ,3 ]
Wang, Sai [4 ]
机构
[1] Citicorp Credit Serv, Long Isl City, NY 11101 USA
[2] Virginia Tech, Dept Stat, Blacksburg, VA 24061 USA
[3] Virginia Tech, Dept Forest Resources & Environm Conservat, Blacksburg, VA 24061 USA
[4] Capital One Financial Corp, Mclean, VA 22102 USA
关键词
Binomial CUSUM Chart; Moving Window; Shewhart np-Chart; Statistical Process Control; Steady State Average Number of Observations to Signal; Subgroup; Surveillance; STATISTICAL PROCESS-CONTROL; QUALITY-CONTROL; CHANGE-POINT; CUSUM; SYSTEMS; DESIGN; SHIFTS;
D O I
10.1080/00224065.2012.11917895
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This paper considers a control chart for monitoring a process proportion based on a generalized likelihood ratio (GLR) statistic. The objective is to effectively detect a wide range of shift sizes. The GLR statistic is obtained from a moving window of past sample statistics that follow a binomial distribution. The Phase II performance of this chart is evaluated using the steady state average number of observations to signal (SSANOS) for detecting sustained increases in the proportion. Comparisons of the binomial GLR chart to the Shewhart np-chart, the individual binomial cumulative sum (CUSUM) chart, and combinations of multiple binomial CUSUM charts show that the overall performance of the binomial GLR chart is at least as good as these other options. Moreover, unlike the other charts, the binomial GLR chart has an advantage that it does not require users to specify multiple charting parameters that may be difficult to obtain, and this makes it easier for the GLR chart to be designed for practical applications.
引用
收藏
页码:192 / 208
页数:17
相关论文
共 50 条
  • [31] Modified control chart for monitoring the variance
    Landim, Tiago Ruckert
    Jardim, Felipe Schoemer
    Oprime, Pedro Carlos
    [J]. BRAZILIAN JOURNAL OF OPERATIONS & PRODUCTION MANAGEMENT, 2021, 18 (03):
  • [32] A Robust Control Chart for Monitoring Dispersion
    Zhou, Maoyuan
    Geng, Wei
    [J]. JOURNAL OF APPLIED MATHEMATICS, 2013,
  • [33] Pattern matching for control chart monitoring
    Cantone, Domenico
    Faro, Simone
    [J]. PROGRESS IN INDUSTRIAL MATHEMATICS AT ECMI 2006, 2008, 12 : 918 - 922
  • [34] Control chart for monitoring occupational asthma
    Hayati, F
    Maghsoodloo, S
    DeVivo, MJ
    Carnahan, BJ
    [J]. JOURNAL OF SAFETY RESEARCH, 2006, 37 (01) : 17 - 26
  • [35] A NEW PROCESS MONITORING CONTROL CHART
    Yang, Su-Fen
    Yang, Chung-Ming
    [J]. UNCERTAINTY MODELING IN KNOWLEDGE ENGINEERING AND DECISION MAKING, 2012, 7 : 931 - 936
  • [36] Shewhart-EWMA chart for monitoring binomial data subject to shifts of random amounts
    Haridy, Salah
    Benneyan, James C.
    [J]. COMPUTERS & INDUSTRIAL ENGINEERING, 2024, 193
  • [37] A binomial CUSUM chart for monitoring type I right-censored Weibull lifetimes
    Choi, Min-jae
    Lee, Jaeheon
    [J]. KOREAN JOURNAL OF APPLIED STATISTICS, 2016, 29 (05) : 823 - 833
  • [38] Better confidence intervals for a binomial proportion
    Pobocikova, Ivana
    [J]. Communications - Scientific Letters of the University of Žilina, 2010, 12 (01): : 31 - 37
  • [39] An Interface to Monitor Process Variability Using the Binomial ATTRIVAR SS Control Chart
    Violante, Joao Pedro Costa
    Machado, Marcela A. G.
    Mendes, Amanda dos Santos
    Almeida, Tulio S.
    [J]. ALGORITHMS, 2024, 17 (05)
  • [40] The Negative Binomial Exponentially Weighted Moving Average Chart with Estimated Control Limits
    Saghir, Aamir
    Lin, Zhengyan
    [J]. QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, 2015, 31 (02) : 239 - 250