Some Omnibus Exponentially Weighted Moving Average Statistical Process Monitoring Schemes

Domangue, Rickie J.; Patch, Steven C.

doi:10.1080/00401706.1991.10484836

Cited by 123 publications

(48 citation statements)

References 14 publications

(19 reference statements)

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“…The surveillance method EWMA (see e.g. Crowder (1987), Domangue and Patch (1991) and Frisen and Sonesson (2001)), which is designed for a controlled false alarm rate at the same time as the variability is reduced by smoothing, might be an alternative.…”

Section: Smoothingmentioning

confidence: 99%

Detection of Turning Points in Business Cycles

Andersson¹,

Bock²,

Frisén³

2004

Journal of Business Cycle Measurement and Analysis

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Methods for continuously monitoring business cycles are compared. A turn in a leading index can be used to predict a turn in the business cycle. Three likelihood based methods for turning point detection are compared in detail by using the theory of statistical surveillance and by simulations. One of the methods is a parametric likelihood ratio method. Another includes a non-parametric estimation procedure. The third is based on a Hidden Markov Model. Evaluations are made of several features such as knowledge of shape and parameters of the curve, types and probabilities of transitions and smoothing. Results on the expected delay time [of](to) a correct alarm and the predictive value of an alarm are discussed...Business cycle, Monitoring, Optimal, Likelihood ratio, HMM, Non-parametric

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Section: Smoothingmentioning

confidence: 99%

Detection of Turning Points in Business Cycles

Andersson¹,

Bock²,

Frisén³

2004

Journal of Business Cycle Measurement and Analysis

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“…As online measurement and distributed computing systems become a norm in today's SPC applications, more sophisticated CUSUM, EWMA and sequential probability ratio test (SPRT) schemes have also been developed to detect mean shift δ µ and standard deviation shift δ σ . This includes the omnibus EWMA chart7, the MaxEWMA chart8, the WLC chart9, the ABS CUSUM chart10, and some combined CUSUM or EWMA schemes which is composed of several individual CUSUM or EWMA charts11, 12.…”

Section: Introductionmentioning

confidence: 99%

A comparison study on effectiveness and robustness of control charts for monitoring process mean and variance

et al. 2011

Quality & Reliability Eng

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This article compares the effectiveness and robustness of nine typical control charts for monitoring both process mean and variance, including the most effective optimal and adaptive sequential probability ratio test (SPRT) charts. The nine charts are categorized into three types (theX type, CUSUM type and SPRT type) and three versions (the basic version, optimal version and adaptive version). While the charting parameters of the basic charts are determined by common wisdoms, the parameters of the optimal and adaptive charts are designed optimally in order to minimize an index average extra quadratic loss for the best overall performance. Moreover, the probability distributions of the mean shift and standard deviation shift are studied explicitly as the influential factors in a factorial experiment. The main findings obtained in this study include: (1) From an overall viewpoint, the SPRT-type chart is more effective than the CUSUM-type chart andX-type chart by 15 and 73%, respectively; (2) in general, the adaptive chart outperforms the optimal chart and basic chart by 16 and 97%, respectively; (3) the optimal CUSUM chart is the most effective fixed sample size and sampling interval chart and the optimal SPRT chart is the best choice among the adaptive charts; and (4) the optimal sample sizes of both theX charts and the CUSUM charts are always equal to one. Furthermore, this article provides several design tables which contain the optimal parameter values and performance indices of 54 charts under different specifications.

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“…Many control schemes for joint monitoring of the process mean and dispersion are based on the EWMA procedure. Domangue and Patch8 presented omnibus EWMA, which is an EWMA on absolute value of standardized sample mean of the observations, to detect simultaneous changes in the process. However, this chart does not determine the source and direction of the process changes.…”

Section: Introductionmentioning

confidence: 99%

The Max EWMAMS control chart for joint monitoring of process mean and variance with individual observations

Memar¹,

Niaki²

2010

Quality & Reliability Eng

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A traditional approach to monitor both the location and the scale parameters of a quality characteristic is to use two separate control charts. These schemes have some difficulties in concurrent tracking and interpretation. To overcome these difficulties, some researchers have proposed schemes consisting of only one chart. However, none of these schemes is designed to work with individual observations. In this research, an exponentially weighted moving average (EWMA)-based control chart that plots only one statistic at a time is proposed to simultaneously monitor the mean and variability with individual observations. The performance of the proposed scheme is compared with one of the two other existing combination charts by simulation. The results show that in general the proposed chart has a significantly better performance than the other combination charts.

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Some Omnibus Exponentially Weighted Moving Average Statistical Process Monitoring Schemes

Cited by 123 publications

References 14 publications

Detection of Turning Points in Business Cycles

Detection of Turning Points in Business Cycles

A comparison study on effectiveness and robustness of control charts for monitoring process mean and variance

The Max EWMAMS control chart for joint monitoring of process mean and variance with individual observations

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