A Class of Distribution-Free Exponentially Weighted Moving Average Schemes for Joint Monitoring of Location and Scale Parameters

Song, Zhi; Mukherjee, Amitava; Marozzi, Marco; Zhang, Jiujun

doi:10.1007/978-3-030-25081-2_6

Cited by 12 publications

(10 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The CG version of the PML 0.25 scheme may be viewed as the CG scheme based on the Lepage type statistic based on Gastwrith score as in Kössler. 21 Further, three CG-Cucconi schemes are introduced using three decompositions of the Cucconi statistic proposed by Song et al 15 . Overall performance of one of the CG-Cucconi scheme, namely the CG-C 1 , is found attractive for a class of joint location-scale shift.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, Song et al 15 showed that it is possible to decompose the Cucconi statistic as a quadratic combination of a location statistic and a scale statistic. The decomposition of the Cucconi statistic can be done in three different ways as:…”

Section: Some Cucconi-type Statistics For Signal Classificationmentioning

confidence: 99%

“…In this paper, we introduce and compare a class of CG charts for post signal classification of signal type, whether the signal is an indicator of location shift or scale shift or a shift in both. Precisely, this paper proposes new CG charts based on three variants of the Cucconi statistic, as in Song et al., 15 CG charts based on the percentile modified Lepage (PML) statistic as in Mukherjee and Sen, 13 Mukherjee and Marozzi 16 and Song et al., 17 and a CG chart based on bias‐corrected Lepage statistic. The original Lepage and Cucconi statistics are less sensitive to small decreasing scale shifts and are prone to display an average run length (ARL) bias.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A class of new nonparametric circular‐grid charts for signal classification

Song

Mukherjee

et al. 2021

Quality & Reliability Eng

Self Cite

View full text Add to dashboard Cite

In this paper, some efficient monitoring and post-signal follow-up approaches are studied and compared for joint surveillance of location and scale of a process using the notions of circular-grid (CG) schemes. Precisely, three variants of CG Cucconi schemes are introduced and compared with three variants of percentile modified Lepage (PML) schemes. One of the PML schemes is equivalent to the traditional CG Lepage scheme, while another may be viewed as the Lepage type statistic using Gastwrith score, which is also a powerful tool for process surveillance. Overall, one of the proposed CG Cucconi schemes is most effective in identifying a class of signals, whether it is a location shift or scale shift or a shift in both parameters. It also indicates the direction of the shifts in either or both the parameters. Detecting a downward scale shift is the most challenging task in joint monitoring, and to this end, a new bias-corrected CG Lepage scheme is introduced. We compare the competing schemes in terms of correct signal classification probabilities. We illustrate the use of the proposed schemes in monitoring the trip-duration data in cab services. Some concluding remarks and future research problems are offered.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Some Cucconi-type Statistics For Signal Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A class of new nonparametric circular‐grid charts for signal classification

Song

Mukherjee

et al. 2021

Quality & Reliability Eng

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition, Song et al (2020b) investigated and compared six nonparametric EWMA charts for joint monitoring the location and scale parameters of the underlying process. All schemes considered are based on Lepage-type statistics.…”

Section: Nonparametric Ewma Control Schemes Based On Lepage-type Statisticsmentioning

confidence: 99%

“…By utilizing the test statistics defined in Eq. ( 95), Song et al (2020b) proposed the following EWMA statistics:…”

Section: Nonparametric Ewma Control Schemes Based On Lepage-type Statisticsmentioning

confidence: 99%

Nonparametric EWMA-Type Control Charts for Monitoring Industrial Processes: An Overview

Triantafyllou¹,

Ram²

2021

Int J Math, Eng, Manag Sci

View full text Add to dashboard Cite

In the present paper we provide an up-to-date overview of nonparametric Exponentially Weighted Moving Average (EWMA) control charts. Due to their nonparametric nature, such memory-type schemes are proved to be very useful for monitoring industrial processes, where the output cannot match to a particular probability distribution. Several fundamental contributions on the topic are mentioned, while recent advances are also presented in some detail. In addition, some practical applications of the nonparametric EWMA-type control charts are highlighted, in order to emphasize their crucial role in the contemporary online statistical process control.

show abstract

A comprehensive distribution‐free scheme for tri‐aspect surveillance of complex processes

Mukherjee

Qiu

Marozzi

2021

Appl Stoch Models Bus & Ind

Self Cite

View full text Add to dashboard Cite

Distribution-free charting schemes for process monitoring are more robust and reliable than their parametric counterparts when the process distributions are unknown and complicated. Most of the existing control charts are uni-aspect schemes because they can detect a shift in only one aspect of the process distribution, like location or scale. Research on schemes for simultaneous surveillance of location and scale parameters has been very active in recent years, leading to many bi-aspect schemes. These schemes do not explicitly deal with the shape of the process distribution. However, a shift in the shape parameter with or without a location or scale shift can occur in practice, especially in production or time-to-event processes. Note that there are nonparametric schemes for detecting general shifts based on goodness-of-fit test statistics or empirical likelihood ratio statistics, which cannot isolate if there is a shift in location, scale, shape, or combination of these parameters. This article introduces a new distribution-free process monitoring scheme that can detect a shift in the location, scale or shape parameters, or any combinations of them. The new scheme is based on a combined statistic designed via Euclidean distance of the standardized Wilcoxon statistic for location, the standardized Ansari-Bradley statistic for scale, and the standardized Savage-type statistic for shape. We discuss the implementation design using the average run-length as the performance metric and investigate the proposed scheme's in-control performance. It is shown that the new charting scheme is in-control robust irrespective of the underlying process distribution and therefore applicable to monitor any univariate continuous processes. An out-of-control performance comparison study of the new scheme with many existing schemes shows that the new scheme is preferable to the existing schemes. We illustrate the proposed chart with real data to monitor a passenger train's arrival delays in Italy's regional route. The proposed scheme is comprehensive because it integrates the follow-up procedure via integrated sub-charts for classifying the signaling component.

show abstract

A Class of Distribution-Free Exponentially Weighted Moving Average Schemes for Joint Monitoring of Location and Scale Parameters

Cited by 12 publications

References 42 publications

A class of new nonparametric circular‐grid charts for signal classification

A class of new nonparametric circular‐grid charts for signal classification

Nonparametric EWMA-Type Control Charts for Monitoring Industrial Processes: An Overview

A comprehensive distribution‐free scheme for tri‐aspect surveillance of complex processes

Contact Info

Product

Resources

About