Fast initial response features for EWMA control charts

Knoth, Sven

doi:10.1007/bf02762034

Cited by 65 publications

(35 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Steiner (1999) concluded that, in general, exact time-varying control limits are useful when λ is small, say, less than 0.3. For an elaborate discussion on the differences between asymptotic and exact time-varying limits, we refer Knoth (2003Knoth ( , 2005. The choice of the two design parameters, λ and L, for the proposed charts is deliberated in more detail in Section 3.1.…”

Section: Statistical Background: Npewma-ex Chartmentioning

confidence: 99%

Design and implementation issues for a class of distribution-free Phase II EWMA exceedance control charts

Graham

Mukherjee

Chakraborti

2016

International Journal of Production Research

View full text Add to dashboard Cite

Distribution-free (nonparametric) control charts can play an essential role in process monitoring when there is dearth of information about the underlying distribution. In this paper, we study various aspects related to an efficient design and execution of a class of nonparametric Phase II exponentially weighted moving average (denoted by NPEWMA) charts based on exceedance statistics. The choice of the Phase I (reference) sample order statistic used in the design of the control chart is investigated. We use the exact time-varying control limits and the median run-length as the metric in an in-depth performance study. Based on the performance of the chart, we outline implementation strategies and make recommendations for selecting this order statistic from a practical point of view and provide illustrations with a dataset. We conclude with a summary and some remarks.

show abstract

Section: Statistical Background: Npewma-ex Chartmentioning

confidence: 99%

Design and implementation issues for a class of distribution-free Phase II EWMA exceedance control charts

Graham

Mukherjee

Chakraborti

2016

International Journal of Production Research

View full text Add to dashboard Cite

show abstract

“…However, the experience of the authors shows that the maximal loss of efficiency in the case of the use of the asymptotic matrix is about 0.5% of the value of the in-control ARL. For a detailed discussion of this issue see Knoth [33]. Using the limit the control statistic is given by…”

Section: Design Of the Control Chartsmentioning

confidence: 99%

Surveillance of the covariance matrix of multivariate nonlinear time series

Śliwa

Schmid

2005

Statistics

View full text Add to dashboard Cite

In this paper, sequential procedures for the surveillance of the covariance matrices of multivariate nonlinear time series are introduced. Two different types of control charts are proposed. The first type is based on the exponential smoothing of each component of a local measure for the covariances. The control statistic is equal to the Mahalanobis distance of this quantity with its in-control mean. In our second approach, the Mahalanobis distance is first determined and after that it is exponentially smoothed. We discuss three examples of local measures.Several properties of the proposed schemes are discussed assuming the target process to be generated by a multivariate GARCH(1, 1) model. The generalization to the family of spherical distributions allows the modelling of frequently observed fat tails in financial data. Some results of an extensive Monte Carlo simulation study are provided in order to judge the performance of the presented control schemes. As a performance measure we use the average run length. An empirical example illustrates the importance of the fast detection of the changes in the covariance structure of the returns of financial assets.

show abstract

“…As a result of this research new charts were developed. For example, the EWMA chart [3,4], CUSUM chart [5,6] and the synthetic-X [7][8][9][10]. These charts exhibit better performance in detecting small shifts, although they are more complex to use.…”

Section: Introductionmentioning

confidence: 96%