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On the statistical performance of quality control charts with estimated parameters / Nesma Ali Mahmoud Saleh ; Supervised Mahmoud Alsaid Mahmoud

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Nesma Ali Mahmoud Saleh , 2016Description: 133 P. : facsimiles ; 25cmOther title:
  • حول الأداء الإحصائي لخرائط التحكم في حالة المعلمات المقدرة [Added title page title]
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  • Issued also as CD
Dissertation note: Thesis (Ph.D.) - Cairo University - Faculty of Economics and Political Science - Department of Statistics Summary: Under estimated in-control parameters, the Phase II control chart performance is expected to vary among practitioners due to the use of different Phase I data sets. Accordingly, the typical measure of Phase II control chart performance, the average run length (ARL), becomes a random variable. In the literature, control charts with estimated parameters were assessed and the appropriate amounts of Phase I data were recommended based on the in-control performance averaged across the practitioner-to-practitioner variability. In this study, aspects of the ARL distribution, such as the standard deviation of the average run length (SDARL) and some quantiles are used to quantify the between-practitioner variability in control charts performance when the process parameters are estimated. It is shown that no realistic amount of Phase I data is sufficient to have confidence that the attained in-control ARL is close to the desired value. Moreover, it is shown that even with the use of larger amounts of historical data, there is still a problem with the excessive false alarm rates. Due to the extreme difficulty of lowering the variation in the in-control ARLs, an alternative design criterion based on the bootstrap approach is recommended for adjusting the control limits. The technique is quite effective in controlling the percentage of short in-control ARLs resulting from the estimation error. Three of the most well-known univariate control charts (Shewhart, EWMA, and CUSUM), and two multivariate charts (T2, and MEWMA) are studied
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Item type Current library Home library Call number Copy number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.03.01.Ph.D.2016.Ne.O (Browse shelf(Opens below)) Not for loan 01010110070820000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.03.01.Ph.D.2016.Ne.O (Browse shelf(Opens below)) 70820.CD Not for loan 01020110070820000

Thesis (Ph.D.) - Cairo University - Faculty of Economics and Political Science - Department of Statistics

Under estimated in-control parameters, the Phase II control chart performance is expected to vary among practitioners due to the use of different Phase I data sets. Accordingly, the typical measure of Phase II control chart performance, the average run length (ARL), becomes a random variable. In the literature, control charts with estimated parameters were assessed and the appropriate amounts of Phase I data were recommended based on the in-control performance averaged across the practitioner-to-practitioner variability. In this study, aspects of the ARL distribution, such as the standard deviation of the average run length (SDARL) and some quantiles are used to quantify the between-practitioner variability in control charts performance when the process parameters are estimated. It is shown that no realistic amount of Phase I data is sufficient to have confidence that the attained in-control ARL is close to the desired value. Moreover, it is shown that even with the use of larger amounts of historical data, there is still a problem with the excessive false alarm rates. Due to the extreme difficulty of lowering the variation in the in-control ARLs, an alternative design criterion based on the bootstrap approach is recommended for adjusting the control limits. The technique is quite effective in controlling the percentage of short in-control ARLs resulting from the estimation error. Three of the most well-known univariate control charts (Shewhart, EWMA, and CUSUM), and two multivariate charts (T2, and MEWMA) are studied

Issued also as CD

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