Statistical Surveillance. Optimality and Methods

Frisén, Marianne

doi:10.1111/j.1751-5823.2003.tb00205.x

Cited by 133 publications

(112 citation statements)

References 146 publications

(213 reference statements)

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“…(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995). He is the recipient of the Box Medal (2012) , Shewhart Medal (2002), Jack Youden Prize (1995, 2003, and Brumbaugh Award (2000,2006). He is a Fellow of the American Statistical Association, a Fellow of the American Society for Quality, and an elected member of the International Statistical Institute.…”

Section: Resultsmentioning

confidence: 99%

“…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%

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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: Resultsmentioning

confidence: 99%

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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“…We assume that the out-of-control situation is present from the beginning of the monitoring, i.e. the change and the surveillance start at the same time [25]. We summarize the run length distribution by the popular average run length (ARL), which is the expected duration until the first alarm and depends on the shift height ∆, ARL(∆) = E ∆ (R) [26].…”

Section: Selected Criteria For the Run Length Analysismentioning

confidence: 99%

Control charts for the mean based on robust two-sample tests

Fried

2016

Journal of Statistical Computation and Simulation

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We propose and investigate robust control charts for the detection of sudden shifts in sequences of very noisy observations with a naturally slowly varying mean. They sequentially apply local two-sample tests for the location problem. Thus, no previous knowledge about the in-control behaviour is necessary. We identify critical values for the tests to achieve a desired in-control average run length (ARL0) with extensive simulations. Control charts based on nonparametric tests or a randomization principle provide a satisfactory run length behaviour for different error distributions. They possess a nearly distribution-free ARL0 and are fast in detecting present signal jumps in a time series. In our simulations and exemplary real-world applications from biosignal analysis, a test based on the two-sample Hodges-Lehmann estimator leads to very promising results regarding distribution independence, robustness and detection speed.

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“…The utility expression above is easily applied to the univariate situation. If the function h(t A -τ) is a constant, b, then it has been shown that the utility is maximized when the delay, t A -τ, is minimized (see Frisén (2003)). The minimal expected delay was shown to hold also for a situation where τ is not Geometrically distributed (Andersson (2004)).…”

Section: Simultaneous Solutionmentioning

confidence: 99%

Effect of Dependency in Systems for Multivariate Surveillance

Andersson

2009

Communications in Statistics - Simulation and Computation

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In many situations we need a system for detecting changes early. Examples are early detection of disease outbreaks, of patients at risk and of financial instability. In influenza outbreaks, for example, we want to detect an increase in the number of cases. Important indicators might be the number of cases of influenza-like illness and pharmacy sales (e.g. aspirin). By continually monitoring these indicators, we can early detect a change in the process of interest. The methodology of statistical surveillance is used. Often, the conclusions about the process(es) of interest is improved if the surveillance is based on several indicators. Here three systems for multivariate surveillance are compared. One system, called LRpar, is based on parallel likelihood ratio methods, since the likelihood ratio has been shown to have several optimality properties. In LRpar, the marginal density of each indicator is monitored and an alarm is called as soon as one of the likelihood ratios exceeds its alarm limit. The LRpar is compared to an optimal alarm system, called LRjoint, which is derived from the full likelihood ratio for the joint density. The performances of LRpar and LRjoint are compared to a system where the Hotellings T2 is monitored. The evaluation is made using the delay of a motivated alarm, as a function of the times of the changes. The effect of dependency is investigated: both dependency between the monitored processes and correlation between the time points when the changes occur. When the first change occurs immediately, the three methods work rather similarly, for independent processes and zero correlation between the change times. But when all processes change later, the T2 has much longer delay than LRjoint and LRpar. This holds both when the processes are independent and when they have a positive covariance. When we assume a positive correlation between the change times, the LRjoint yields a shorter delay than LRpar when the changes actually do occur simultaneously, whereas the opposite is true when the changes do actually occur at different time point.

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Statistical Surveillance. Optimality and Methods

Cited by 133 publications

References 146 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

Control charts for the mean based on robust two-sample tests

Effect of Dependency in Systems for Multivariate Surveillance

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