A mean deviation-based approach to monitor process variability

Riaz, Muhammad; Saghir, Aamir

doi:10.1080/00949650802174397

Cited by 33 publications

(39 citation statements)

References 38 publications

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“…Since it is difficult to obtain E(MD) analytically, it is obtained by simulation. Extensive tables for t 2 (n) can be found in Riaz and Saghir (2009). The advantage of this estimator is that it is less sensitive to outliers than R (cf.…”

Section: Estimators Of the Standard Deviationmentioning

confidence: 99%

A Robust Standard Deviation Control Chart

Schoonhoven

Does

2012

Technometrics

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A robust standard deviation control chart Schoonhoven, M.; Does, R.J.M.M. General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden.The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. This article studies the robustness of Phase I estimators for the standard deviation control chart. A Phase I estimator should be efficient in the absence of contaminations and resistant to disturbances. Most of the robust estimators proposed in the literature are robust against either diffuse disturbances, that is, outliers spread over the subgroups, or localized disturbances, which affect an entire subgroup. In this article, we compare various robust standard deviation estimators and propose an algorithm that is robust against both types of disturbances. The algorithm is intuitive and is the best estimator in terms of overall performance. We also study the effect of using robust estimators from Phase I on Phase II control chart performance. Additional results for this article are available online as Supplementary Material.

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Section: Estimators Of the Standard Deviationmentioning

confidence: 99%

A Robust Standard Deviation Control Chart

Schoonhoven

Does

2012

Technometrics

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“…Recently, Riaz and Saghir [26] proposed a Shewhart-type dispersion control chart based on the MD which they named the MD chart. They showed that the MD chart is superior to both R and S charts in terms of its power for detecting shifts in process variability.…”

Section: Introductionmentioning

confidence: 99%

MDEWMA chart: an efficient and robust alternative to monitor process dispersion

Abbasi

Miller

2013

Journal of Statistical Computation and Simulation

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Control chart is the most important statistical process control tool used to monitor changes in process location and dispersion. In this study, an EWMA control chart is proposed for efficient and robust monitoring of process dispersion. The proposed chart, namely the MDEWMA chart, is based on estimating the process standard deviation (σ ) using the mean absolute deviations (MD), taken from the sample median. The performance of the proposed chart has been compared with the EWMASR chart (a dispersion EWMA chart based on sample range) and MD chart (a Shewhart-type dispersion chart based on MD), under the existence and violation of normality assumption. It has been observed that the proposed MDEWMA chart is more efficient and robust when compared with both EWMASR and MD charts in terms of run length (RL) characteristics such as average RL, median RL and standard deviation of the RL distribution.

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“…Because it is di cult to obtain the constant t 2 (n) analytically, it has to be obtained by simulation. Extensive tables of t 2 (n) can be found in Riaz and Saghir (2009). Like G, we can rewrite ADM as a function of order statistics.…”

Section: Estimators Of the Standard Deviationmentioning

confidence: 99%

Design and Analysis of Control Charts for Standard Deviation with Estimated Parameters

Schoonhoven

Riaz

Does³

2011

Journal of Quality Technology

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Design and analysis of control charts for standard deviation with estimated parametersSchoonhoven, M.; Riaz, M.; Does, R.J.M.M. General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. This paper concerns the design and analysis of the standard deviation control chart with estimated limits. We consider an extensive range of statistics to estimate the in-control standard deviation (Phase I) and design the control chart for real-time process monitoring (Phase II) by determining the factors for the control limits. The Phase II performance of the design schemes is assessed when the Phase I data are uncontaminated and normally distributed as well as when the Phase I data are contaminated. We propose a robust estimation method based on the mean absolute deviation from the median supplemented with a simple screening method. It turns out that this approach is e cient under normality and performs substantially better than the traditional estimators and several robust proposals when contaminations are present.

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A mean deviation-based approach to monitor process variability

Cited by 33 publications

References 38 publications

A Robust Standard Deviation Control Chart

A Robust Standard Deviation Control Chart

MDEWMA chart: an efficient and robust alternative to monitor process dispersion

Design and Analysis of Control Charts for Standard Deviation with Estimated Parameters

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