Multivariate semiparametric control charts for mixed-type data

A useful tool that has gained popularity in the Quality Control area is the control chart which monitors a process over time, identifies potential changes, understands variations, and eventually improves the quality and performance of the process. This article introduces a new class of multivariate...

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Bibliographic Details
Published in:Statistical methods in medical research 2023-04, Vol.32 (4), p.671-690
Main Authors: Sofikitou, Elisavet M, Markatou, Marianthi, Koutras, Markos V
Format: Article
Language:English
Subjects:
Algorithms
Averages
Charts
Clustering
Continuity (mathematics)
Control charts
False alarms
Multivariate analysis
Popularity
Process controls
Quality control
Random variables
Statistical process control
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Summary:A useful tool that has gained popularity in the Quality Control area is the control chart which monitors a process over time, identifies potential changes, understands variations, and eventually improves the quality and performance of the process. This article introduces a new class of multivariate semiparametric control charts for monitoring multivariate mixed-type data, which comprise both continuous and discrete random variables (rvs). Our methodology leverages ideas from clustering and Statistical Process Control to develop control charts for MIxed-type data. We propose four control chart schemes based on modified versions of the KAy-means for MIxed LArge KAMILA data clustering algorithm, where we assume that the two existing clusters represent the reference and the test sample. The charts are semiparametric, the continuous rvs follow a distribution that belongs in the class of elliptical distributions. Categorical scale rvs follow a multinomial distribution. We present the algorithmic procedures and study the characteristics of the new control charts. The performance of the proposed schemes is evaluated on the basis of the False Alarm Rate and in-control Average Run Length. Finally, we demonstrate the effectiveness and applicability of our proposed methods utilizing real-world data.
ISSN:0962-2802
1477-0334
DOI:10.1177/09622802221142528