Multi-stage change point detection with copula conditional distribution with pca and functional pca

Jong Min Kim, Ning Wang, Yumin Liu

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish (US)
Article number1777
Pages (from-to)1-25
Number of pages25
Issue number10
StatePublished - Oct 2020

Bibliographical note

Funding Information:
Funding: This research was funded by National Natural Science Foundation of China Grant (No. 71672182, No. U1604262 and No. U1904211) and National Social Science Fund of China (No. 20BTJ059).

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.


  • Copula
  • Function principal component analysis
  • Multivariate change point detection
  • Principal component analysis


Dive into the research topics of 'Multi-stage change point detection with copula conditional distribution with pca and functional pca'. Together they form a unique fingerprint.

Cite this