The total median statistic to monitor contaminated normal data

Figueiredo, Fernanda; Gomes, M. Ivette

doi:10.1080/16843703.2016.1139840

Cited by 8 publications

(3 citation statements)

References 21 publications

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“…In SPM applications, in which in‐control process distribution is assumed to be skewed/long‐tailed, robust statistics were found to show good performance for determining CLs in traditional control charts, cumulative sum, and EWMA structures, or in constructing robust control chars. () Similarly, for in‐control processes assumed to consist of normally distributed data with temporary outliers in Phase I (the view also adopted in the current study), performance of “outlier resistant statistics,” such as trimmed mean (TM) and interquartile range (IQR), in estimating of CLs was shown to surprass that of traditional estimators. () Although asymptotic convergence properties were questioned, incorporation robust statistics into a reweighting scheme was shown to improve monitoring performance, possibly due to desirable finite sample properties of the reweighted robust estimators .…”

Section: Introductionmentioning

confidence: 83%

Revisiting reweighted robust standard deviation estimators for univariate Shewhart S‐charts

Mutlu

Alakent

2018

Quality & Reliability Eng

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Phase I outliers, unless screened during process parameter estimation, are known to deteriorate Phase II performance of process control charts. Reweighting estimators, ie, trimming outlier subgroups and individual observations, were suggested in the literature to improve both the robustness and efficiency of the resulting parameter estimates. In the current study, effects of various reweighted estimators at different trimming levels on the Phase II performance of S‐charts are elucidated using computer simulations including isolated and mixtures of contamination models. Outlier magnitudes in the simulations are held at a moderately low level to mimic industrial practice. Subtleties, such as varying Type I error rate among different trimming levels with respect to quantiles of dispersion estimates, prevent a single method to be revealed as the best performing one under all circumstances, and choice of estimators and trimming levels should depend on the number of subgroups in Phase I and the specifics of the process. Nevertheless, S‐chart using scale M‐estimator with logistic ρ and location M‐estimator at 2% trimming generally stands out in terms of Phase II performance, and high trimming levels are particularly recommended for high number of Phase I subgroups.

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Section: Introductionmentioning

confidence: 83%

Revisiting reweighted robust standard deviation estimators for univariate Shewhart S‐charts

Mutlu

Alakent

2018

Quality & Reliability Eng

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“…On this topic, and following Fernanda Figueiredo's PhD thesis, already referred in 3.5.1, I first mention some articles essentially in the area of Statistical Quality Control (SQC), but in which various methods are used involving Order Statistics and Extremes: [94], with details on the role of moving maxima and the total median in SQC; [95,96], on the role of asymmetric-normal distributions in SQC and in eliminating risk in modelling real data; [97], on the role of the total median in monitoring contaminated normal data, subject to various types of contamination; [93], with the development of acceptance sampling plans to reduce the risk associated with chemical compounds.…”

Section: Extremes In Statistical Quality Controlmentioning

confidence: 99%

The PORTSEA (Portuguese School of Extremes and Applications) and a few personal scientific achievements

Gomes

2024

Communications in Mathematics

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The Portuguese School of Extremes and Applications is nowadays well recognised by the international scientific community, and in my opinion, the organisation of a NATO Advanced Study Institute on Statistical Extremes and Applications, which took place at Vimeiro in the summer of 1983, was a landmark for the international recognition of the group. The dynamic of publication has been very high and the topics under investigation in the area of Extremes have been quite diverse. In this article, attention will be paid essentially to some of the scientific achievements of the author in this field.

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“…The implementation of commonly used control charts is considerably less wise when normality assumption is seriously violated [9]- [12]. To accommodate this nonnormality situation, a varielty of new designs and modification of existing designs have been proposed in literature [13]. For instance, control chart based on the assumption that observed quality characteristic followed the lognormal distribution has been developed [14].…”

Section: Introductionmentioning

confidence: 99%

On Scale Parameter Monitoring of the Rayleigh Distributed Data Using a New Design

et al. 2020

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In recent years, many supplementary designs have been developed incorporating the assumption that data follow the particular non-normal distribution. The-control chart is one such design proposed to monitor the parameter of the single parameter Rayleigh distributed data. Commonly, in authentic situations, practitioners need to estimate the scale parameter in the observed processes instead of. However, the positive square root of the-statistic used in the existing design of-control chart is not an unbiased estimator of and thus could not be practiced to monitor the scale parameter of the Rayleigh distributed process. A new structure of the-control chart namely for monitoring the scale parameter of the Rayleigh distributed data has been originally developed in this study. The statistical basis of this newly design in terms of average run length (), characteristic function and power curve have been derived. The analytical results are utilized further to determine the parameters of-chart and in comparing the performance of the proposed control chart with existing competitors. Comparative results illustrate the effectiveness of the proposed design in view of statistical power. Finally, the computational procedure of this newly-chart has been demonstrated using simulated data and real data on the breaking strength of carbon fibers.

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The total median statistic to monitor contaminated normal data

Cited by 8 publications

References 21 publications

Revisiting reweighted robust standard deviation estimators for univariate Shewhart S‐charts

Revisiting reweighted robust standard deviation estimators for univariate Shewhart S‐charts

The PORTSEA (Portuguese School of Extremes and Applications) and a few personal scientific achievements

On Scale Parameter Monitoring of the Rayleigh Distributed Data Using a New Design

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