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Gower distance-based multivariate control charts for a mixture of continuous and categorical variables

机译:基于高尔距离的多变量控制图,包含连续变量和分类变量

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Processes characterized by high dimensional and mixture data challenge traditional statistical process control charts. In this study, we propose a multivariate control chart based on the Gower distance that can handle a mixture of continuous and categorical data. An extensive simulation study was conducted to examine the properties of the proposed control chart under various scenarios and compared it with some existing multivariate control charts. The simulation results revealed that the proposed control chart outperformed the existing charts when the number of categorical variables increases. Furthermore, we demonstrated the applicability and effectiveness of the proposed control charts through a real case study.
机译:以高维和混合数据为特征的过程对传统的统计过程控制图提出了挑战。在这项研究中,我们提出了一个基于高尔距离的多元控制图,它可以处理连续数据和分类数据的混合。进行了广泛的仿真研究,以检查所提出的控制图在各种情况下的属性,并将其与一些现有的多元控制图进行比较。仿真结果表明,当分类变量的数量增加时,所提出的控制图要优于现有的控制图。此外,我们通过实际案例研究证明了所提出的控制图的适用性和有效性。

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