| 000 | 02669cam a2200301 a 4500 | ||
|---|---|---|---|
| 003 | EG-GiCUC | ||
| 008 | 170206s2016 ua h f m 000 0 eng d | ||
| 040 |
_aEG-GiCUC _beng _cEG-GiCUC |
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| 041 | 0 | _aeng | |
| 049 | _aDeposite | ||
| 097 | _aPh.D | ||
| 099 | _aCai01.03.01.Ph.D.2016.Ne.O | ||
| 100 | 0 | _aNesma Ali Mahmoud Saleh | |
| 245 | 1 | 0 |
_aOn the statistical performance of quality control charts with estimated parameters / _cNesma Ali Mahmoud Saleh ; Supervised Mahmoud Alsaid Mahmoud |
| 246 | 1 | 5 | _aحول الأداء الإحصائي لخرائط التحكم في حالة المعلمات المقدرة |
| 260 |
_aCairo : _bNesma Ali Mahmoud Saleh , _c2016 |
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| 300 |
_a133 P. : _bfacsimiles ; _c25cm |
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| 502 | _aThesis (Ph.D.) - Cairo University - Faculty of Economics and Political Science - Department of Statistics | ||
| 520 | _aUnder 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 | ||
| 530 | _aIssued also as CD | ||
| 653 | 4 | _aBootstrap | |
| 653 | 4 | _aControl Charts | |
| 653 | 4 | _aEstimation Effect | |
| 700 | 0 |
_aMahmoud Alsaid Mahmoud , _eSupervisor |
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| 905 |
_aNazla _eRevisor |
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| 905 |
_aShaima _eCataloger |
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| 942 |
_2ddc _cTH |
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| 999 |
_c59701 _d59701 |
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