Robust Monitoring of Contaminated Data

Davis, Cali M.; Adams, Benjamin M.

doi:10.1080/00224065.2005.11980314

Cited by 16 publications

(8 citation statements)

References 7 publications

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“…He proposed a method, constructed around a variant of the biweight A estimator, that is resistant to both diffuse and localized disturbances. Finally, Davis and Adams (2005) proposed a diagnostic technique for monitoring data that might be contaminated with outliers to react to signals that indicate a true process shift only.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

A Robust Standard Deviation Control Chart

Schoonhoven

Does

2012

Technometrics

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“…The third option for further work is to compare our approach with other alternatives. Robust control charts based on diagnostic statistics could perhaps be adapted to cross‐sectional data. The same goes for adjusted p ‐charts and the recently proposed general solution for control charts for over‐dispersed data .…”

Section: Discussionmentioning

confidence: 99%

Outlier Detection for Healthcare Quality Monitoring – A Comparison of Four Approaches to Over‐Dispersed Proportions

Vidmar

Blagus

2013

Quality & Reliability Eng

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bOutlier detection among over-dispersed proportions is important in healthcare quality monitoring. We had previously introduced control limits for double-square-root chart on the basis of prediction intervals from regression-through-origin and compared our approach to common outlier detection tests. In this study, we develop our approach further by adjusting the confidence level (in the spirit of Chauvenet's criterion and Bayesian thinking) and transforming the chart into an asymmetric funnel plot. We compare it to Laney's approach (p'-chart adapted for cross-sectional data), Spiegelhalter's approach (funnel plots based on multiplicative or additive regression models) and Carling's median rule. Comparisons are performed on simulated and real data. The simulations comprise 'small' (<0.2; highly right-skewed) and 'large' (>0.5; symmetrically distributed) proportions, drawn in samples of size 10-100 from lognormal distribution either without outliers or with one outlier added. The real data comprise hospital readmissions from the UK (used by Laney and Spiegelhalter) and business indicators of healthcare quality for Slovenian hospitals. In the simulations, Spiegelhalter's approach tended to yield very high false alarm rates, except the multiplicative version in very small samples. Laney's approach produced fewest false alarms but could not detect the outlier in very small samples among small proportions, and regardless of sample size among large proportions. Median rule performed similarly. Our approach performed the best overall, although it is slightly less liberal than median rule for small proportions, it appears to be the only generally useful approach for large proportions.

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“…Examples of robust estimation methods in Phase I control charts include Rocke, 6 Rocke, 7 Tatum, 8 Vargas 9 and Davis and Adams. 10 The effect of using these robust estimators on Phase II performance is not clear, but it is likely to be inferior to the use of standard estimates because robust estimators are generally not as efficient. ' The motivation and inspiration for this study has been taken from Abbassi and Miller, 4 which emphasized for further investigation on the efficiency and capability of dispersion control charts in the presence of the contaminated environments.…”

Section: Introductionmentioning

confidence: 99%

Control Charts for Process Dispersion Parameter under Contaminated Normal Environments

Ali

Mahmood

Nazir

et al. 2015

Quality & Reliability Eng

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Control charts are important statistical tool used to monitor fluctuations in the process location and dispersion parameters. The issues relating to the appropriate choice of control charts for the effective detection of process variability are addressed, and different control chart structures, such as Shewhart‐type, exponentially weighted moving average and cumulative sum are explored under ideal assumption of normality and contaminated normal environments, and hence, those control charts structures are identified which are more capable to detect aberrant changes in the process dispersion. Copyright © 2015 John Wiley & Sons, Ltd.

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Robust Monitoring of Contaminated Data

Cited by 16 publications

References 7 publications

A Robust Standard Deviation Control Chart

A Robust Standard Deviation Control Chart

Outlier Detection for Healthcare Quality Monitoring – A Comparison of Four Approaches to Over‐Dispersed Proportions

Control Charts for Process Dispersion Parameter under Contaminated Normal Environments

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