An EWMA for Monitoring a Process Standard Deviation

The existing optimal design of the fixed sampling interval S Reka bentuk optimum carta kawalan EWMA-S 2 selang pensampelan tetap yang digunakan untuk memantau proses sampel varians adalah berdasarkan kriteria panjang larian purata (ARL). Oleh sebab bentuk taburan panjang larian berubah dengan magnitud anjakan dalam varians, maka panjang larian median (MRL) memberi penjelasan yang lebih bermakna tentang prestasi terkawal dan luar kawalan carta kawalan. Kertas kerja ini mencadangkan reka bentuk optimum untuk

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“…λ < 0.050) causes numerical difficulty in evaluating the MRLs. This setback was also pointed out by Crowder and Hamilton (1992) for the ARL case.…”

Section: Introductionmentioning

confidence: 86%

A Study on the S2-EWMA Chart for Monitoring the Process Variance based on the MRL Performance

Teh

Khoo

Ong

et al. 2015

JSM

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“…However, the EWMA method has also been studied in many other situations. The case of a shift in the variance was studied by Chang and Gan (1994), Crowder and Hamilton (1992) and Morais and Pacheco (1998). The case of a simultaneous shift in both the mean and the variance has been studied by Domangue and Patch (1991), Gan (1995) and Morais and Pacheco (2000).…”

Section: Different Types Of Ewma Methodsmentioning

confidence: 99%

Evaluations of some Exponentially Weighted Moving Average methods

Sonesson¹

2003

Journal of Applied Statistics

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SUMMARYSeveral versions of the EWMA (Exponentially Weighted Moving Average) method for monitoring a process with the aim of detecting a shift in the mean are studied both for the onesided and the two-sided case. The effects of using barriers for the one-sided alarm statistic are also studied. One important issue is the effect of different types of alarm limits. Different measures of evaluation are considered such as the expected delay, the ARLI, the probability of successful detection and the predictive value of an alarm to give a broad picture of the features of the methods. Results are presented both for a fixed ARLO and a fixed probability of a false alarm. The differences highlight the essential problem of how to define comparability between surveillance methods. The results are from a large-scale simulation study. Special attention is given to the effect on the confidence in the final results by the stochastic variation in the calibration of the methods. It appears that important differences from an inferential point of view exist between the one-and two-sided versions of the methods. It is demonstrated that the method, usually considered as a convenient approximation, is to be preferred over the exact version in many respects.

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“…Often the transformation is made such that the variable under surveillance is (approximately) Gaussian and standard charts for Gaussian variables therefore can be used. Examples such of transformations are the logarithm of the subgroup standard deviation (Crowder and Hamilton (1992)) and |X(t)/σ 2 | 1/2 (Hawkins (1981)). …”

Section: Suggested Statistics Under Surveillancementioning

confidence: 99%

“…Crowder and Hamilton (1992), MacGregor and Harris (1993), Acosta-Mejía and Pignatiello (2000) and Schipper and Schmid (2001b). The EWMA method is not likelihood based and is not studied here.…”

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confidence: 99%

Evaluations of Likelihood‐Based Surveillance of Volatility

Bock

2007

Financial Surveillance

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The volatility of asset returns are important in finance. Different likelihood based methods of statistical surveillance for detecting a change in the variance are evaluated. The differences are how the partial likelihood ratios are weighted. The full likelihood ratio, Shiryaev-Roberts, Shewhart and the CUSUM methods are derived in case of an independent and identically distributed Gaussian process. The behavior of the methods is studied both when there is no change and when the change occurs at different time points. The false alarms are controlled by the median run length. Differences and limiting equalities of the methods are shown. The performances when the process parameters for which the methods are optimized for differ from the true values of the parameters are evaluated. The methods are illustrated on a period of Standard and Poor's 500 stock market index.

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An EWMA for Monitoring a Process Standard Deviation

Cited by 279 publications

References 11 publications

A Study on the S2-EWMA Chart for Monitoring the Process Variance based on the MRL Performance

A Study on the S2-EWMA Chart for Monitoring the Process Variance based on the MRL Performance

Evaluations of some Exponentially Weighted Moving Average methods

Evaluations of Likelihood‐Based Surveillance of Volatility

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