An explanatory study on the non-parametric multivariate T2 control chart

Mostajeran, A.; Iranpanah, N.; Noorossana, Rassoul

doi:10.22237/jmasm/1529418622

Cited by 10 publications

(7 citation statements)

References 36 publications

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“…Both types of control chart produced out-of-control signals from different points, hence inconclusive results obtained. This study proposes the application of non-parametric multivariate T 2 control chart as an alternative tool for process monitoring whenever the multivariate normality assumption is violated (Mostajeran & Iranpanah, 2018;Boone & Chakraborti, 2012).…”

Section: Discussionmentioning

confidence: 99%

An Application of Univariate and Multivariate Control Charts in Monitoring Water Quality

Hashim¹,

Andrew²,

Malandi³

2020

ASMSJ

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Control chart is a tool for detecting an out-of-control signal in statistical process control (SPC). It is widely used in process monitoring in order to detect changes in process mean or process dispersion. This study aims to illustrate the application of multivariate control charts in monitoring water quality at one of the water treatments plants in Kota Kinabalu, Sabah. The tested water quality variables in this study are turbidity, pH value, dissolved oxygen (DO) and concentration of ferum. Two multivariate control charts, Hotelling’sT2 and MCUSUM control charts are constructed under the violation of the multivariate normality assumption. The purpose is to study the effect of non-normal data upon the monitoring process using the selected multivariate control charts. By comparing the monitoring process between the two types of control charts, the consistency of the results is studied. All the univariate and multivariate control charts produced out-of-control signals from different points, hence inconclusive results obtained. Keywords: Water quality; multivariate control chart; univariate control chart; Hotelling’s T2; MCUSUM

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Section: Discussionmentioning

confidence: 99%

An Application of Univariate and Multivariate Control Charts in Monitoring Water Quality

Hashim¹,

Andrew²,

Malandi³

2020

ASMSJ

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“…However, unlike the principal component scores produced by a standard PCA for only numerical variables, the scores produced by PCA Mix do not follow multivariate normal distribution or any known family of distribution (Ashan et al 2018). To solve this problem, we use a nonparametric methodbootstrap resampling (Mostajeran et al 2018;Phaladiganon et al 2011)to estimate the control limit for the 2 chart based on PCA Mix. Bootstrap resampling, first introduced by Efron (1979), is one of the most widely-used methods to estimate the parameters of an unknown distribution.…”

Section: Introductionmentioning

confidence: 99%

Assessing Response Quality by Using Multivariate Control Charts for Numerical and Categorical Response Quality Indicators

Jin

Loosveldt

2020

Journal of Survey Statistics and Methodology

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When assessing interview response quality to identify potentially low-quality interviews, both numerical and categorical response quality indicators (mixed indicators) are usually available. However, research on how to use them simultaneously is very rare. In the current article, we extend the application of conventional multivariate control charts to include response quality indicators that are of a mixed type. We analyze data from the eighth round of the European Social Survey in Belgium, characterized by six numerical and two categorical response quality indicators. First, we employ a principal component analysis mix procedure (PCA Mix) to transform the mixed quality indicators into principal components. The principal component scores are subsequently used to construct a Hotelling T2 statistic. To deal with the non-multivariate normal nature of the principal component scores obtained from the PCA Mix, a nonparametric bootstrap method is then applied to calculate the control limit for the T2 statistic. Second, we suggest tools to interpret an identified outlier in terms of finding the responsible original indicator(s). Third, we present a cyclic procedure for determining the “in-control” data, by iteratively removing the outliers until the process is considered as being in control. Lastly, we identify the most important indicators that discriminate the outliers from the in-control data. Our results imply that multivariate control charts based on relevant projection tools such as PCA Mix in combination with the bootstrap technique have great potential for use in evaluating interview response quality and identifying outliers.

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“…In Mostajeran et al [7], the authors presented the use of non-parametric bootstrap multivariate control charts |S|, W, and G, and this method is grounded upon the use of bootstrapped data in the estimation of the in-control state. In this study, the authors succeeded in obtaining satisfactory performance of bootstrap [12] demonstrated the application of a bootstrap multivariate control chart and compared it with a Hotelling's T 2 parametric multivariate control chart, a multivariate sign control chart, and a multivariate Wilcoxon control chart. A simulation study was employed for the purpose.…”

Section: Introductionmentioning

confidence: 99%

Bivariate modified hotelling’s T2 charts using bootstrap data

Haddad¹,

Alsmadi²,

Badawi³

et al. 2019

IJECE

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The conventional Hotelling’s charts are evidently inefficient as it has resulted in disorganized data with outliers, and therefore, this study proposed the application of a novel alternative robust Hotelling’s charts approach. For the robust scale estimator , this approach encompasses the use of the Hodges-Lehmann vector and the covariance matrix in place of the arithmetic mean vector and the covariance matrix, respectively. The proposed chart was examined performance wise. For the purpose, simulated bivariate bootstrap datasets were used in two conditions, namely independent variables and dependent variables. Then, assessment was made to the modified chart in terms of its robustness. For the purpose, the likelihood of outliers’ detection and false alarms were computed. From the outcomes from the computations made, the proposed charts demonstrated superiority over the conventional ones for all the cases tested.

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An explanatory study on the non-parametric multivariate T2 control chart

Cited by 10 publications

References 36 publications

An Application of Univariate and Multivariate Control Charts in Monitoring Water Quality

An Application of Univariate and Multivariate Control Charts in Monitoring Water Quality

Assessing Response Quality by Using Multivariate Control Charts for Numerical and Categorical Response Quality Indicators

Bivariate modified hotelling’s T2 charts using bootstrap data

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