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dc.contributor.author | Human, Schalk William![]() |
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dc.contributor.author | Kritzinger, Pierre![]() |
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dc.contributor.author | Chakraborti, Subhabrata![]() |
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dc.date.accessioned | 2012-05-02T08:48:01Z | |
dc.date.available | 2012-10-31T00:20:03Z | |
dc.date.issued | 2011-10 | |
dc.description.abstract | The traditional exponentially weighted moving average (EWMA) chart is one of the most popular control charts used in practice today. The in-control robustness is the key to the proper design and implementation of any control chart, lack of which can render its out-of-control shift detection capability almost meaningless. To this end, Borror et al. [5] studied the performance of the traditional EWMA chart for the mean for i.i.d. data. We use a more extensive simulation study to further investigate the in-control robustness (to non-normality) of the three different EWMA designs studied by Borror et al. [5]. Our study includes a much wider collection of non-normal distributions including light- and heavy-tailed and symmetric and asymmetric bi-modal as well as the contaminated normal, which is particularly useful to study the effects of outliers. Also, we consider two separate cases: (i) when the process mean and standard deviation are both known and (ii) when they are both unknown and estimated from an in-control Phase I sample. In addition, unlike in the study done by Borror et al. [5], the average run-length (ARL) is not used as the sole performance measure in our study, we consider the standard deviation of the run-length (SDRL), the median run-length (MDRL), and the first and the third quartiles as well as the first and the 99th percentiles of the in-control run-length distribution for a better overall assessment of the traditional EWMA chart’s in-control performance. Our findings sound a cautionary note to the (over) use of the EWMA chart in practice, at least with some types of non-normal data. A summary and recommendations are provided. | en |
dc.description.librarian | nf2012 | en |
dc.description.sponsorship | STATOMET and the Department of Statistics at the University of Pretoria. | en_US |
dc.description.uri | http://www.tandfonline.com/loi/cjas20 | en_US |
dc.identifier.citation | Human, SW, Kritzinger, P & Chakraborti, S 2011, 'Robustness of the EWMA control chart for individual observations', Journal of Applied Statistics, vol. 38, no. 10, pp. 2071-2087. | en |
dc.identifier.issn | 0266-4763 (print) | |
dc.identifier.issn | 1360-0532 (online) | |
dc.identifier.other | 10.1080/02664763.2010.545114 | |
dc.identifier.uri | http://hdl.handle.net/2263/18650 | |
dc.language.iso | en | en_US |
dc.publisher | Taylor & Francis | en_US |
dc.rights | © 2011 Taylor & Francis. This is an electronic version of an article published in Journal of Applied Statistics, vol. 38, no. 10, pp. 2071-2087, October 2011. Journal of Applied Statistics is available online at: http://www.tandfonline.com/loi/cjas20. | en_US |
dc.subject | Average run-length | en |
dc.subject | Box-plots | en |
dc.subject | Distribution-free statistics | en |
dc.subject | Median run-length | en |
dc.subject | Percentiles | en |
dc.subject | EWMA control chart | en |
dc.subject.lcsh | Nonparametric statistics | en |
dc.subject.lcsh | Process control -- Statistical methods | en |
dc.subject.lcsh | Statistics -- Simulation methods | en |
dc.subject.lcsh | Robust control | en |
dc.title | Robustness of the EWMA control chart for individual observations | en |
dc.type | Postprint Article | en |