Rong Pan is an Associate Professor of Industrial Engineering in the School of Computing, Informatics, and Decision Systems Engineering at Arizona State University. He received his Ph.D. degree in Industrial Engineering from Penn State University in 2002. His research interests include failure time data analysis, design of experiments, multivariate statistical process control, time series analysis, and computational Bayesian methods. His research has been supported by NSF, Arizona Science Foundation, Air Force Research Lab, etc. He has published 45 journal papers and over 30 refereed conference papers. He was the recipient of the Stan Ofsthum Award, presented by the Society of Reliability Engineers, in 2008 and 2011, and the William A. Golomski Award, presented by the IIE Quality Control & Reliability Engineering Division, in 2015. His papers won the Best Reliability Paper Award of Quality Engineering in 2012 and 2013. Rong Pan is a senior member of ASQ and IIE, and a member of SRE, IEEE and INFORMS. He serves on the editorial boards of Journal of Quality Technology and Quality Engineering.

Steven E Rigdon is Professor of Biostatistics at Saint Louis University. He Is the author of nearly 100 articles in journals, conference proceedings, book chapters, etc. He is the author of Calculus, 8th Edition, Calculus, 9th Edition, Calculus: Early Transcendentals (all published by Pearson), and Statistical Methods for the Reliability of Repairable Systems (published by Wiley). Prior to joining Saint Louis University, he was Distinguished Research Professor at Southern Illinois University Edwardsville (SIUE). He now has emeritus status at SIUE.

Charles W. Champ is a Professor of Statistics at Georgia Southern University. He received his PhD from the University of Louisiana-Lafayette. His research areas include statistical quality control and statistically designed experiments. He has published over seventy journal articles, conference proceedings, book chapters, and technical reports.

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.

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.