Two simple Shewhart‐type multivariate nonparametric control charts

Boone, J. M.; Chakraborti, S.

doi:10.1002/asmb.900

Cited by 50 publications

(39 citation statements)

References 25 publications

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“…For this purpose, we generated data from a multivariate normal and t(5) distribution with parameters The choice of the parameters was based on the publication of Boone and Chakraborti 15 , who have presented a related simulation study for bivariate nonparametric control charts.…”

Section: Comparison To Existing Control Chartsmentioning

confidence: 99%

“…Recently, Boone and Chakraborti 15 (refer also to Boone 16 ) proposed two Shewhart-type multivariate nonparametric charts: the multivariate sign control chart (abbrev. SN 2 chart) and the multivariate Wilcoxon signed-rank control chart (abbrev.…”

Section: Introductionmentioning

confidence: 99%

“…In Section 5, we compare the new chart to the traditional parametric χ 2 chart and also to the nonparametric SΝ 2 and SR 2 charts proposed by Boone and Chakraborti. 15 For this purpose, a simulation study was carried out. In addition, several tables and graphs are provided for assessing the efficiency of the new chart and facilitating practitioners for addressing the design problem when they wish to exploit it.…”

Section: Introductionmentioning

confidence: 99%

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A Bivariate Semiparametric Control Chart Based on Order Statistics

Koutras

Sofikitou

2016

Quality & Reliability Eng

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In this article, a new bivariate semiparametric Shewhart-type control chart is presented. The proposed chart is based on the bivariate statistic (X (r) , Y (s) ), where X (r) and Y (s) are the order statistics of the respective X and Y test samples. It is created by considering a straightforward generalization of the well-known univariate median control chart and can be easily applied because it calls for the computation of two single order statistics. The false alarm rate and the in-control run length are not affected by the marginal distributions of the monitored characteristics. However, its performance is typically affected by the dependence structure of the bivariate observations under study; therefore, the suggested chart may be characterized as a semiparametric control chart.An explicit expression for the operating characteristic function of the new control chart is obtained. Moreover, exact formulae are provided for the calculation of the alarm rate given that the characteristics under study follow specific bivariate distributions. In addition, tables and graphs are given for the implementation of the chart for some typical average run length values and false alarm rates. The performance of the suggested chart is compared with that of the traditional χ 2 chart as well as to the nonparametric SN 2 and SR 2 charts that are based on the multivariate form of the sign test and the Wilcoxon signed-rank test, respectively. Finally, in order to demonstrate the applicability of our chart, a case study regarding a realworld problem related to winery production is presented.

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Section: Comparison To Existing Control Chartsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Bivariate Semiparametric Control Chart Based on Order Statistics

Koutras

Sofikitou

2016

Quality & Reliability Eng

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“…Zamba and Hawkins (2006) proposed a control chart based on a likelihood ratio test when the in-control parameters are unknown. Qiu and Hawkins (2001), Qiu (2008), Zou and Tsung (2011), Boone and Chakraborti (2012), and Holland and Hawkins (2014) proposed multivariate nonparametric or distribution-free control charts. Khoo and Quah (2003), Vargas and Lagos (2007), Yeh et al (2012), Yen et al (2012), and Li et al (2013) dealt with multivariate control charts for monitoring the process covariance matrix.…”

Section: Introductionmentioning

confidence: 99%

Multivariate EWMA control chart based on a variable selection using AIC for multivariate statistical process monitoring

Nishimura

Matsuura

Suzuki

2015

Statistics & Probability Letters

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“…There are several books and papers on the use of statistical tools and technics for improving quality and productivity (Box et al, 1978;Chen, 1997;Xie & Goh, 1999;Jones et al, 2001;Montgomery & Runger, 2003;Bamford & Greatbanks, 2005;Ehie & Sheu, 2005;He & Grigoryan, 2005;Chakraborti, 2006;Triefenbach, 2008;Chakraborti et al, 2008;Hoerl & Snee, 2010;Boone & Chakraborti, 2011;Zhang et al, 2011;Oprime & Ganga, 2013;Montgomery, 2013;Bataineh & Al-Dwairi, 2012). Employees have started to join improvement groups, mainly through Kaizen programs, and they have been trained in statistical technics of quality monitoring and control.…”

Section: Introductionmentioning

confidence: 99%

A bibliometric analysis of 50 years of worldwide research on statistical process control

et al. 2016

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Resumo: Um crescente número de trabalhos sobre controle estatístico do processo (CEP) emergiu nos últimos cinquentaAbstract: An increasing number of papers on statistical process control (SPC) has emerged in the last fifty years, especially in the last fifteen years. This may be attributed to the increased global competitiveness generated by innovation and the continuous improvement of products and processes. In this sense, SPC has a fundamentally important role in quality and production systems. The research in this paper considers the context of technological improvement and innovation of products and processes to increase corporate competitiveness. There are several other statistical technics and tools for assisting continuous improvement and innovation of products and processes but, despite the limitations in their use in the improvement projects, there is growing concern about the use of SPC. A gap between the SPC technics taught in engineering courses and their practical applications to industrial problems is observed in empirical research; thus, it is important to understand what has been done and identify the trends in SPC research. The bibliometric study in this paper is proposed in this direction and uses the Web of Science (WoS) database. Data analysis indicates that there was a growth rate of more than 90% in the number of publications on SPC after 1990. Our results reveal the countries where these publications have come from, the authors with the highest number of papers and their networks. Main sources of publications are also identified; it is observed that the publications of SPC papers are concentrated in some of the international research journals, not necessarily those with the major high-impact factors. Furthermore, the papers are focused on industrial engineering, operations research and management science fields. The most common term found in the papers was cumulative sum control charts, but new topics have emerged and have been researched in the past ten years, such as multivariate methods for process monitoring and nonparametric methods.

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Two simple Shewhart‐type multivariate nonparametric control charts

Cited by 50 publications

References 25 publications

A Bivariate Semiparametric Control Chart Based on Order Statistics

A Bivariate Semiparametric Control Chart Based on Order Statistics

Multivariate EWMA control chart based on a variable selection using AIC for multivariate statistical process monitoring

A bibliometric analysis of 50 years of worldwide research on statistical process control

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