استخدام الخرائط اللامعلمية متعددة المتغيرات في الرقابة الاحصائية على الانتاج

علي عبد العاطي, فاطمة; حميد رشيد, محمد

doi:10.21608/alat.2016.218135

	استخدام الخرائط اللامعلمية متعددة المتغيرات في الرقابة الاحصائية على الانتاج "دراسة تطبيقية"
المجلة المصرية للدراسات التجارية
Article 11, Volume 40, Issue 3, July 2016, Page 415-437 PDF (1.06 MB)
Document Type: المقالة الأصلية
DOI: 10.21608/alat.2016.218135
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Authors
فاطمة علي عبد العاطي; محمد حميد رشيد
کلية التجارة - جامعة المنصورة
Abstract
Control charts that are typically based on the assumption of a specific form of a parametric distribution, such as the normal, are called parametric control charts. however, there is not enough information to justify this assumption the distribution assumption of the data is not met or there is not enough evidence showing that the assumption is met. It is well known that the performance of many parametric control charts can be seriously degraded in situations like this. Thus, control charts that do not require a specific distributional assumption to be valid, the so-called nonparametric or distribution-free charts, are desirable in practice. Nonparametric charts have increasingly become viable alternatives to parametric counterparts in detecting process shifts when the underlying process output distribution is unknown, specifically when the process measurement is multivariate. In this paper, two simple to use multivariate nonparametric control charts are considered. The charts are Shewhart-type charts and are based on the multivariate forms of the sign and the Wilcoxon signed-rank tests. The charts were compared with those of multivariable control parametric chart exists.
Keywords
Wilcoxon signed-rank statistic; false alarm rate; normal distribution; multivariate quality control; nonparametric statistics


References
1- Amin, R.W. and Searcy, A.J., 1991. A nonparametric exponentially weighted moving average control scheme. Communications in Statistics-Simulation and Computation, 20(4), pp.1049-1072. 2- Amin, R.W., Reynolds Jr, M.R. and Saad, B., 1995. Nonparametric quality control charts based on the sign statistic. Communications in Statistics-Theory and Methods, 24(6), pp.1597-1623. 3- Bakir, S.T., 2005. A distribution-free Shewhart quality control chart based on signed-ranks. Quality control and applied statistics, 50(3), pp.243-246. 4- Bakir, S.T., 2006. Distribution-free quality control charts based on signed-rank-like statistics. Communications in Statistics—Theory and Methods,35(4), pp.743-757. 5- Boone, J.M. and Chakraborti, S., 2012. Two simple Shewhart‐type multivariate nonparametric control charts. Applied Stochastic Models in Business and Industry, 28(2), pp.130-140. 6- Boone, J.M., 2010. Contributions to multivariate control charting: Studies of the Z chart and four nonparametric 23 charts (Doctoral dissertation, The University of Alabama TUSCALOOSA). 7- Chakraborti, S., Van der Laan, P. and Bakir, S.T., 2001. Nonparametric control charts: an overview and some results. Journal of Quality Technology,33(3), p.304. 8- Cherubini, U. Luciano, E. Vecchiato, W., 2004. Copula Methods in Finance. Wiley: NJ 9- Das, N., 2009. A new multivariate non-parametric control chart based on sign test. Quality Technology and Quantitative Management, 6(2), pp.155-169. 10- Gibbons, J.D. and Chakraborti, S., 2003.Nonparametric Statistical Inference. Marcel Dekker: New York. 11- Graham, M.A., Chakraborti, S. and Human, S.W., 2011. A nonparametric exponentially weighted moving average signed-rank chart for monitoring location. Computational Statistics & Data Analysis, 55(8), pp.2490-2503. 12- Hack1, P. and Ledolter, J., 1992. A new nonparametric quality control technique. Communications in Statistics-Simulation and Computation, 21(2), pp.423-443. 13- Hackl, P. and Ledolter, J., 1991. A control chart based on ranks. Journal of Quality Technology, 23(2), pp.117-124. 14- Hettmansperger, T.P., 2006. Multivariate location tests. Encyclopedia of Statistical Sciences. 15- Johnson, N.L. and Kotz, S., 1976. Distributions in Statistics: Continuous Multivariate Distributions. Wiley: New York. 16- Kotz, S. and Nadarajah, S., 2004. Multivariate t-distributions and their applications. Cambridge University Press. 17- Lowry, C.A., Woodall, W.H., Champ, C.W. and Rigdon, S.E., 1992. A multivariate exponentially weighted moving average control chart.Technometrics, 34(1), pp.46-53. 18- Pignatiello Jr, J.J., 1993. Strategies for robust multiresponse quality engineering. IIE transactions, 25(3), pp.5-15. 19- Puri, M.L. and Pranab Kumar, S., 1993. Nonparametric methods in multivariate analysis. 24 20- Randles, R.H., Hettmansperger, T.P. and Casella, G., 2004. Introduction to the special issue: Nonparametric Statistics. Statistical Science, 19(4), pp.561-561. 21- Stoumbos, Z.G., Reynolds Jr, M.R., Ryan, T.P. and Woodall, W.H., 2000. The state of statistical process control as we proceed into the 21st century.Journal of the American Statistical Association, 95(451), pp.992-998. 22- Woodall, W. H. and Montgomery, D., 2013. Some Current Directions in the Theory and Application of Statistical Process Monitoring, Journal of Quality Technology, to appear. 23- Yang, S.F., Lin, J.S. and Cheng, S.W., 2011. A new nonparametric EWMA sign control chart. Expert Systems with Applications, 38(5), pp.6239-6243.
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استخدام الخرائط اللامعلمية متعددة المتغيرات في الرقابة الاحصائية على الانتاج "دراسة تطبيقية"