Multi-Stage Change Point Detection with Copula Conditional Distribution with PCA and Functional PCA

A global uncertainty environment, such as the COVID-19 pandemic, has affected the manufacturing industry severely in terms of supply and demand balancing. So, it is common that one stage statistical process control (SPC) chart affects the next-stage SPC chart. It is our research objective to conside...

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Bibliographic Details
Published in:Mathematics (Basel) 2020-10, Vol.8 (10), p.1777
Main Authors: Kim, Jong-Min, Wang, Ning, Liu, Yumin
Format: Article
Language:English
Subjects:
Control charts
copula
Food science
function principal component analysis
Manufacturing
Multivariate analysis
multivariate change point detection
Normal distribution
principal component analysis
Principal components analysis
Quality control
Random variables
Statistical analysis
Statistical methods
Statistical process control
Time series
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Summary:A global uncertainty environment, such as the COVID-19 pandemic, has affected the manufacturing industry severely in terms of supply and demand balancing. So, it is common that one stage statistical process control (SPC) chart affects the next-stage SPC chart. It is our research objective to consider a conditional case for the multi-stage multivariate change point detection (CPD) model for highly correlated multivariate data via copula conditional distributions with principal component analysis (PCA) and functional PCA (FPCA). First of all, we review the current available multivariate CPD models, which are the energy test-based control chart (ETCC) and the nonparametric multivariate change point model (NPMVCP). We extend the current available CPD models to the conditional multi-stage multivariate CPD model via copula conditional distributions with PCA for linear normal multivariate data and FPCA for nonlinear non-normal multivariate data.
ISSN:2227-7390
2227-7390
DOI:10.3390/math8101777