On Assessing the Performance of Sequential Procedures for Detecting a Change

Kenett, Ron S.; Pollak, Moshe

doi:10.1002/qre.1436

Cited by 34 publications

(30 citation statements)

References 40 publications

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“…In passive monitoring actions to affect the process are less immediate and effective and the monitoring statistic is not reset after a signal (see, e.g., in public health surveillance). For an elaborate discussion see Frisén (2003) or Kenett and Pollak (2012). Throughout this article, we focus on the time until the first false alarm under the assumption of a stable process.…”

Section: Performance Measuresmentioning

confidence: 99%

A head-to-head comparative study of the conditional performance of control charts based on estimated parameters

Zwetsloot

Woodall

2016

Quality Engineering

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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. ABSTRACTImplementation of the Shewhart, CUSUM, and EWMA charts requires estimates of the in-control process parameters. Many researchers have shown that estimation error strongly influences the performance of these charts. However, a given amount of estimation error may differ in effect across charts. Therefore, we perform a pairwise comparison of the effect of estimation error across these charts. We conclude that the Shewhart chart is more strongly affected by estimation error than the CUSUM and EWMA charts. Furthermore, we show that the general belief that the CUSUM and EWMA charts have similar performance no longer holds under estimated parameters.

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Section: Performance Measuresmentioning

confidence: 99%

A head-to-head comparative study of the conditional performance of control charts based on estimated parameters

Zwetsloot

Woodall

2016

Quality Engineering

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“…Zoom on Figure d: The predicted values (solid line) coincide with a function f of the form f ( t ) = ar t + bt + c (dotted lines) during the period…”

Section: Investigation Of Robust Monitoring For Trended Time Series Wmentioning

confidence: 88%

Robust Monitoring of an Industrial IT System in the Presence of Structural Change

Padonou

Roustant

Lutz

2014

Quality & Reliability Eng

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This paper presents an original research initiated by the monitoring needs of a semiconductor production plant. The industrial operations rely on an Information Technology (IT) system, and several time series data are controlled statistically. Unfortunately, these variables often contain outliers, as well as structural changes due to external decisions in the IT activity. As a consequence, it has been observed that the monitoring results obtained with standard techniques could be severely biased. This paper presents some contributions to overcome such difficulties. A new monitoring method is proposed, based on robust Holt-Winters smoothing algorithm, and coupled with a relearning procedure for structural breaks detection. Such a method is flexible enough for a large-scale industrial application. We evaluate its performances through simulations studies, and show its usefulness in industrial real applications for univariate and multivariate time series. The scope of application deals with IT activity monitoring, but the introduced statistical methods are generic enough for being used in other industrial fields.

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“…When

\overset{t}{true} \neq 1

, the A R L is an inadequate metric to assess detection speed since the performance of the monitoring statistic is likely to depend on when the fault started. Therefore, we have used the conditional expected delay ( C E D ) proposed by Frisén and Kenett and Pollak calculated as

C E D false(\overset{t}{true} false) = E ([], T_{t} - \overset{t}{true} + 1 false| T_{t} \geq \overset{t}{true}),

where T t is the moment when the fault is detected. When there are no out‐of‐control observations, we use A R L 0 , and when

\overset{t}{true} = 1

, the A R L and C E D are the same.…”

Section: Comparison Of Chart Performancementioning

confidence: 99%

A new multivariate variability control chart based on a covariance matrix combination

Alfaro

Álvarez

2018

Appl Stoch Models Bus & Ind

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In the field of multivariate quality control, there are many control charts related to the process mean but few options addressing process variability. Variability control charts have two main drawbacks: the first relates to the number of parameters to tune and the second relates to how changes in the mean affect the performance of these charts. Thus, in this paper, we propose a new multivariate variability control chart, called the multivariate exponentially weighted covariance matrix combination, which solves these two problems. The results show that this new chart performs well in the detection of changes in variance when the mean does not change and outperforms other charts when the mean does change. KEYWORDS average run length (ARL), conditional expected delay (CED), individual observations, Monte Carlo simulation, multivariate exponentially weighted covariance matrix combination Appl Stochastic Models Bus Ind. 2019;35:823-836.wileyonlinelibrary.com/journal/asmb

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On Assessing the Performance of Sequential Procedures for Detecting a Change

Cited by 34 publications

References 40 publications

A head-to-head comparative study of the conditional performance of control charts based on estimated parameters

A head-to-head comparative study of the conditional performance of control charts based on estimated parameters

Robust Monitoring of an Industrial IT System in the Presence of Structural Change

A new multivariate variability control chart based on a covariance matrix combination

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