Optimizing joint location-scale monitoring – An adaptive distribution-free approach with minimal loss of information

Song, Zhi; Mukherjee, Amitava; Zhang, Jiujun

doi:10.1016/j.ejor.2018.11.060

Cited by 26 publications

(14 citation statements)

References 45 publications

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“…For future research, we propose the comparison of control charts based on Lepage‐type and Cucconi statistics using the median run length as the performance metric, as it is seen in Graham et al New control charts can be created with the proposed statistic. For example, we can use this statistic in an EWMA scheme to create a control chart based on the change‐point model, as it is seen in Xiang et al; and we can create an adaptive control chart combining the Lepage statistic with the optimal score options for short, medium, and log tail distributions, as it is presented in Song et al Also, Celano and Castagliola designed an EWMA sign control chart for detecting changes in location in finite production horizon processes, which are common in job shop and small lot production. The performance of the proposed Lepage‐type statistic can be studied in these situations.…”

Section: Discussionmentioning

confidence: 99%

A distribution‐free CUSUM chart for joint monitoring of location and scale based on the combination of Wilcoxon and Mood statistics

Tercero-Gómez

Aguilar-Lleyda

Cordero-Franco

et al. 2020

Quality & Reliability Eng

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We propose a distribution-free cumulative sum (CUSUM) chart for joint monitoring of location and scale based on a Lepage-type statistic that combines the Wilcoxon rank sum and the Mood statistics. Monte Carlo simulations were used to obtain control limits and examine the in-control and out-of-control performance of the new chart. A direct comparison of the new chart was made with the original CUSUM Lepage based on Wilcoxon rank sum and Ansari-Bradley statistics. The result is a more powerful chart in most of the considered scenarios and thus a more useful CUSUM chart. An example using real data illustrates how the proposed control chart can be implemented.

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Section: Discussionmentioning

confidence: 99%

A distribution‐free CUSUM chart for joint monitoring of location and scale based on the combination of Wilcoxon and Mood statistics

Tercero-Gómez

Aguilar-Lleyda

Cordero-Franco

et al. 2020

Quality & Reliability Eng

View full text Add to dashboard Cite

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“…The p.d.f and the c.d.f ofẐ * i now can be obtained from ( 30) and ( 31) where γ X , γ Y , ω and ρ are replaced by γX * , γȲ * , ω * and ρ * as defined in ( 23), ( 24), ( 25) and (18).…”

Section: It Can Be Seen From Their Definition Thatxmentioning

confidence: 99%

“…Recent studies related to this problem can be seen in Tran et al [22], Khurshid & Chakraborty [9], Sabahno et al [17], Hassani et al [8], Daryabari et al [7], Song et al [18,19] and Sabahno et al [16]. For the case of control charts monitoring the ratio, Tran et al [22] used a quite strict assumption that z * 1 = τ * z * 0 where z * 1 and z * 0 are the out-of-control ratios, respectively and the in-control ratio in the presence of measurement error and τ is the shift size.…”

Section: Introductionmentioning

confidence: 99%

The effect of measurement errors on the performance of the Exponentially Weighted Moving Average control charts for the Ratio of Two Normally Distributed Variables

Nguyen

Tran

2021

European Journal of Operational Research

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Investigating the effect of measurement errors on the control chart monitoring the ratio of two normal random variables is an important task to facilitate the use of this kind of control chart in practice. Moreover, a deep insight into the problem can help practitioners to find a way to reduce unexpected impacts of measurement errors on the chart performance. This paper provides a study on the performance of the exponentially weighted moving average control chart monitoring the ratio in the presence of measurement errors. We extend the linear covariate error model applied in previous studies to a more general situation, which makes the study more realistic. The numerical results show that although the precision error and the accuracy error have negative influences on the proposed chart performance when these errors are not large these influences are not significant.

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“…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%

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

Song

Mukherjee

et al. 2021

Quality & Reliability Eng

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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.

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Optimizing joint location-scale monitoring – An adaptive distribution-free approach with minimal loss of information

Cited by 26 publications

References 45 publications

A distribution‐free CUSUM chart for joint monitoring of location and scale based on the combination of Wilcoxon and Mood statistics

A distribution‐free CUSUM chart for joint monitoring of location and scale based on the combination of Wilcoxon and Mood statistics

The effect of measurement errors on the performance of the Exponentially Weighted Moving Average control charts for the Ratio of Two Normally Distributed Variables

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

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