Multivariate Statistical Process Control Theory and Applications Data-Enabled Engineering Series

This book provides a summary of the most recent developments in the research of both univariate and multivariate change point problems in the past 20 years. The emphasis is on multivariate process control and process diagnosis. The authors provide the fundamental statistical models and algorithms as well as practical solutions to applications in the manufacturing and healthcare systems. This book can be used as a reference book for anyone who is interested in statistical process control techniques, or a supplementary textbook for graduate-level courses such as Statistical Quality Control, Statistical Modeling of Manufacturing Systems, or Multivariate Statistics.

Introduction. Multivariate Statistics. Bayesian Inference. Change Detection for Univariate Processes. Basic Change Detection Algorithms for Multivariate Processes. Advanced Change Detection Algorithms for Multivariate Processes. Change Point Diagnosis for Multivariate Processes, R Packages for Process Change Detection and Diagnosis. Case Studies.