A new multivariate EWMA control chart via multiple testing

Park, Jangho

doi:10.1016/j.jprocont.2015.01.007

Cited by 28 publications

(14 citation statements)

References 31 publications

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“…Ngai and Zhang [15] proposed a MCUSUM control chart based on projection pursuit. Part and Jun [16] investigated a MEWMA control chart via multiple testing. Qiu and Hawkins [17] proposed a nonparametric MCUSUM procedure for detecting shifts in all directions.…”

Section: The Mewma Chartmentioning

confidence: 99%

A Multivariate Homogeneously Weighted Moving Average Control Chart

et al. 2019

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This paper presents a multivariate homogeneously weighted moving average (MHWMA) control chart for monitoring a process mean vector. The MHWMA control chart statistic gives a specific weight to the current observation, and the remaining weight is evenly distributed among the previous observations. We present the design procedure and compare the average run length (ARL) performance of the proposed chart with multivariate Chi-square, multivariate EWMA, and multivariate cumulative sum control charts. The ARL comparison indicates superior performance of the MHWMA chart over its competitors, particularly for the detection of small shifts in the process mean vector. Examples are also provided to show the application of the proposed chart. INDEX TERMS Average run length, control chart, manufacturing process, quality control, statistical process control.

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Section: The Mewma Chartmentioning

confidence: 99%

A Multivariate Homogeneously Weighted Moving Average Control Chart

et al. 2019

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“…Holm's method does not account for different levels of difficulty of tested hypotheses. However, it can be generalized to allow optimal solutions similar to (4.6) in the following way [36][37][38][39][40][41].…”

Section: General Distribution and Bahadur Efficiencymentioning

confidence: 99%

Weighted Statistics for Testing Multiple Endpoints in Clinical Trials

Baron

2019

ABBA

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Bonferroni, Holm, and Holm-type stepwise approaches have been well developed for the simultaneous testing of multiple hypotheses in medical experiments. Methods exist for controlling familywise error rates at their preset levels. This article shows how performance of these tests can often be substantially improved by accounting for the relative difficulty of tests. Introducing suitably chosen weights optimizes the error spending between the multiple endpoints. Such an extension of classical testing schemes generally results in a smaller required sample size without sacrificing the familywise error rate and power.

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“…In these processes, it is more suitable to adopt multivariate control charts, ie, Hotellings T 2 , multivariate EWMA (MEWMA), and multivariate CUSUM (MCUSUM) charts. Research works on MEWMA charts can be found in Lowry et al, Prabhu and Runger, Alkahtani and Schaffer, Park and Jun, Liang et al, Chiang et al, and Flury and Quaglino, to name a few. Meanwhile, readers can refer to previous studies for research works on the MCUSUM chart.…”

Section: Introductionmentioning

confidence: 99%

Memory‐type multivariate charts with fixed and variable sampling intervals for process mean when covariance matrix is unknown

Haq

Khoo

2019

Quality & Reliability Eng

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Memory‐type multivariate charts have been widely recognized as a potentially powerful process monitoring tool because of their excellent speed in detecting small‐to‐moderate shifts in the mean vector of a multivariate normally distributed process, namely, the multivariate EWMA (MEWMA), double MEWMA, Crosier multivariate CUSUM (MCUSUM), and Pignatiello and Runger MCUSUM charts. These multivariate charts are based on the assumption that the covariance matrix is known in advance; but, it may not be known in practice. It is thus not possible to use these multivariate charts unless a large Phase I dataset is available from an in‐control process. In this paper, we propose multivariate charts with fixed and variable sampling intervals for the process mean vector when the covariance matrix is estimated from sample. Using the Monte Carlo simulation method, the run length characteristics of the multivariate charts are computed. It is shown that the in‐control and out‐of‐control run length performances of the proposed multivariate charts are robust to the changes in the process covariance matrix, while the existing multivariate charts are not. A real dataset is taken to explain the implementation of the proposed multivariate charts.

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A new multivariate EWMA control chart via multiple testing

Cited by 28 publications

References 31 publications

A Multivariate Homogeneously Weighted Moving Average Control Chart

A Multivariate Homogeneously Weighted Moving Average Control Chart

Weighted Statistics for Testing Multiple Endpoints in Clinical Trials

Memory‐type multivariate charts with fixed and variable sampling intervals for process mean when covariance matrix is unknown

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