A distribution‐free control chart for monitoring high‐dimensional processes based on interpoint distances

Shu, Lianjie; Fan, Jinyu

doi:10.1002/nav.21809

Cited by 23 publications

(5 citation statements)

References 42 publications

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“…an apparent second shift in the process mean appearing in Phase II data from sample 1253 to 1567, in which there are 17 non-conforming items. However, similar to the distribution-free control charts proposed by Shu and Fan (2018) and Mukherjee and Marozzi (2020), for most of the nonconforming items, the value of the charting statistics is below the calculated UCL.…”

Section: A Real World Example Datasupporting

confidence: 54%

On Monitoring High-Dimensional Processes with Individual Observations

Ebadi¹,

Chenouri²,

Steiner³

2021

Preprint

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Modern data collecting methods and computation tools have made it possible to monitor high-dimensional processes. In this article, Phase II monitoring of high-dimensional processes is investigated when the available number of samples collected in Phase I is limitted in comparison to the number of variables. A new charting statistic for high-dimensional multivariate processes based on the diagonal elements of the underlying covariance matrix is introduced and a unified procedure for Phase I and II by employing a self-starting control chart is proposed. To remedy the effect of outliers, we adopt a robust procedure for parameter estimation in Phase I and introduce the appropriate consistent estimators. The statistical performance of the proposed method is evaluated in Phase II through average run length (ARL) criterion in the absence and presence of outliers and reveals that the proposed control chart scheme effectively detects various kinds of shifts in the process mean.Finally, we illustrate the applicability of our proposed method via a real-world example.

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Section: A Real World Example Datasupporting

confidence: 54%

On Monitoring High-Dimensional Processes with Individual Observations

Ebadi¹,

Chenouri²,

Steiner³

2021

Preprint

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“…Therefore, traditional one-dimensional or multidimensional parameter control charts [16][17][18][19][20][21][22][23][24] are no longer applicable. Furthermore, while some researchers have proposed high-dimensional nonparametric control charts [25,26], their methods are designed for subgroup data flow, requiring grouped data monitoring (each group typically containing more than 5 samples), which is not suitable for small-sample ovarian cyst data. Consequently, this paper proposes using nonparametric high-dimensional empirical likelihood ratio tests [27] to address high-dimensional and nonparametric data challenges, combined with EWMA control charts for online monitoring.…”

Section: Introductionmentioning

confidence: 99%

Monitoring and early warning of ovarian cancer using high-dimensional non-parametric EWMA control chart based on sliding window

Wu,

Zhong,

Ren

et al. 2024

Preprint

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Ovarian tumors are one of the common ovarian dysfunctions in women, which to some extent affect women's normal work and life. Although ovarian tumors are usually sensitive to chemotherapy, and typically show initial efficacy with platinum/taxane treatment, the postoperative recurrence rate in advanced patients is very high. There have been a considerable number of researchers working to establish new methods to monitor and warn about the progression and prognosis of this malignant tumor disease. One commonly used approach is to first reduce the dimensionality of the data using methods such as principal component analysis, LASSO, deep learning, etc., to select several features most relevant to malignant tumors. Then, either a one-dimensional control chart is used for multiple monitoring and warning of different indicators, or a multivariate control chart is directly used to monitor and warn about the selected indicators. However, in actual data, different features are not completely independent of each other. Using a one-dimensional control chart for multiple monitoring and warning of different indicators may overlook the interactions between different features. Additionally, reducing the dimensionality of the data may result in the loss of some data information, leading to the omission of details, which may affect the accuracy of the model and result in delayed alarms and poor predictive performance. Therefore, this paper proposes a non-parametric monitoring scheme based on high-dimensional empirical likelihood test and sliding window model of EWMA type, for direct online monitoring and warning of high-dimensional ovarian tumor data. We compared this approach with a multivariate EWMA control chart after dimensionality reduction, and Monte Carlo numerical simulation results showed that the high-dimensional non-parametric EWMA monitoring scheme indeed detects tumor data changes faster and issues alarms more promptly than the dimensionality-reduced multivariate EWMA control chart. Furthermore, we further validated the effectiveness of the high-dimensional non-parametric EWMA monitoring scheme in monitoring and warning using tumor resection data from the Third Affiliated Hospital of Soochow University as an example.

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“…Some monitoring schemes have been developed for the Phase II monitoring of high-dimensional process. See, for example, Sun and Tsung (2003), Shu and Fan (2018), Mukherjee and Marozzi (2020), and Li et al (2021). However the Phase I monitoring of high-dimensional process is rarely discussed.…”

Section: Introductionmentioning

confidence: 99%

A phase I change‐point method for high‐dimensional process with sparse mean shifts

Huang

Shu²,

et al. 2023

Naval Research Logistics

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Although Phase I analysis of multivariate processes has been extensively discussed, the discussion on techniques for Phase I monitoring of high-dimensional processes is still limited. In high-dimensional applications, it is common to observe that a large number of components but only a limited number of them change at the same time. The shifted components are often sparse and unknown a priori in practice. Motivated by this, this article studies Phase I monitoring of high-dimensional process mean vectors under an unknown sparsity level of shifts. The basic idea of the proposed monitoring scheme is to first employ the false discovery rate procedure to estimate the sparsity level of mean shifts, and then to monitor the mean changes based on the maximum of the directional likelihood ratio statistics over all the possible shift directions. The comparison results based on extensive simulations favor the proposed monitoring scheme. A real example is presented to illustrate the implementation of the new monitoring scheme.

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A distribution‐free control chart for monitoring high‐dimensional processes based on interpoint distances

Cited by 23 publications

References 42 publications

On Monitoring High-Dimensional Processes with Individual Observations

On Monitoring High-Dimensional Processes with Individual Observations

Monitoring and early warning of ovarian cancer using high-dimensional non-parametric EWMA control chart based on sliding window

A phase I change‐point method for high‐dimensional process with sparse mean shifts

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