Distribution-Free Quality Control Charts Based on Signed-Rank-Like Statistics

Bakir, Saad T.

doi:10.1080/03610920500498907

Cited by 107 publications

(47 citation statements)

References 22 publications

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“…In the last two decades, several researchers studied rank based control charts, see, for example, Qiu and Hawkins (2001), Zou, Wang, and Tsung (2012) among others. Bakir (2004Bakir ( , 2006 proposed a distribution-free Shewhart-type control chart based on signed ranks. Chakraborti and Eryilmaz (2007) considered a Shewhart-type distribution-free control chart for the known IC median of a continuous process distribution.…”

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confidence: 99%

Comparisons of Shewhart-type rank based control charts for monitoring location parameters of univariate processes

Mukherjee

Sen

2015

International Journal of Production Research

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The nonparametric (distribution-free) control charts are robust alternatives to the conventional parametric control charts when the form of underlying process distribution is unknown or complicated. In this paper, we consider two new nonparametric control charts based on the Hogg-Fisher-Randle (HFR) statistic and the Savage rank statistic. These are popular statistics for testing location shifts, especially in right-skewed densities. Nevertheless, the control charts based on these statistics are not studied in quality control literature. In the current context, we study phase-II Shewhart-type charts based on the HFR and Savage statistics. We compare these charts with the Wilcoxon rank-sum chart in terms of false alarm rate, out-of-control average run-length and other run length properties. Implementation procedures and some illustrations of these charts are also provided. Numerical results based on Monte Carlo analysis show that the new charts are superior to the Wilcoxon rank-sum chart for a class of non-normal distributions in detecting location shift. New charts also provide better control over false alarm when reference sample size is small.

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confidence: 99%

Comparisons of Shewhart-type rank based control charts for monitoring location parameters of univariate processes

Mukherjee

Sen

2015

International Journal of Production Research

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“…Nonparametric control charts in multivariate cases have been discussed by Liu (1995), Qiu and Hawkins (2001), Qiu (2008), Zou and Tsung (2011) and Zou, Wang and Tsung (2012). Other researches including Albers and Kallenberg (2004), Bakir (2004Bakir ( , 2006 and Jones, Jordan and Champ (2009) have also contributed to the development of the nonparametric control charts.…”

Section: Introductionmentioning

confidence: 96%

Adaptive nonparametric CUSUM scheme for detecting unknown shifts in location

Liu

Tsung

Zhang

2013

International Journal of Production Research

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In this paper, we provide a sequential rank-based adaptive nonparametric cumulative sum control chart for detecting a range of shifts in the location parameter. This chart is a self-starting scheme, and thus can be used to monitor processes at the start-up stages rather than having to wait for the accumulation of sufficiently large calibration samples. It does not require any prior knowledge of the underlying distribution. The choice of the chart parameters is studied and a simulation study demonstrates that the proposed control chart not only performs robustly for different distributions, but also efficiently detects various magnitudes of shifts. An illustrative example is also given to introduce the implementation of this proposed control chart.

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“…For example, Bakir (2006) has constructed a Shewhart-type control chart using a signed-rank statistic, Chakraborti and Eryilmaz (2007) have considered an alternative class of charts based on the same statistic, while Albers et al (2006) have used a suitably modified version of a nonparametric control chart; for an overview of distribution-free control charts for continuous variables, interested readers are referred to Chakraborti et al (2001).…”

Section: Introductionmentioning

confidence: 99%