Parametric Statistical Change Point Analysis: With Applications to Genetics, Medicine, and Finance - Hardcover

Über die Autorin bzw. den Autor

Qing Wang received the B.Eng. and Ph.D. degrees in control science and engineering from the School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China, in 2013 and 2018, respectively. She is currently a lecturer at the School of Automation, Beijing Institute of Technology. Her current research interests include multi-agent systems, nonlinear systems, intelligent control, and distributed optimization. Bin Xin received the B.S. degree in information engineering and the Ph.D. degree in control science and engineering from the Beijing Institute of Technology, Beijing, China, in 2004 and 2012, respectively. He was an academic visitor at the Decision and Cognitive Sciences Research Centre at the University of Manchester from 2011 to 2012. He is currently a professor at the School of Automation, Beijing Institute of Technology. His current research interests include search and optimization, evolutionary computation, unmanned systems, and multi-agent systems. Jie Chen received the B.S., M.S., and Ph.D. degrees in control theory and control engineering from the Beijing Institute of Technology, Beijing, China, in 1986, 1996, and 2001, respectively. He was the President of Tongji University, Shanghai, China, during 2018-2023. He is a Professor with the Control Science and Engineering, Beijing Institute of Technology and Tongji University, where he serves as the Director of the National Key Laboratory of Autonomous Intelligent Unmanned Systems (KAIUS). He is the academician of the Chinese Academy of Engineering and the fellow of the IEEE and IFAC. His current research interests include complex systems, multiagent systems, multiobjective optimization and decision, and constrained nonlinear control.

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Overall, the book gives a clear and systematic presentation of models and methods. It will be an excellent source for theoretical and applied statisticians who are interested in research on change-point analysis and its applications to many areas. ―Mathematical Reviews (Review of the First Edition)

Revised and expanded, Parametric Statistical Change Point Analysis, Second Edition is an in-depth study of the change point problem from a general point of view, and a deeper look at change point analysis of the most commonly used statistical models. For some time, change point problems have appeared throughout the sciences in such disciplines as economics, medicine, psychology, signal processing, and geology; more recently, they have also been found extensively in applications related to biomedical imaging data, array Comparative Genomic Hybridization (aCGH) data, and gene expression data. These areas of interest―new and old―have motivated substantial research on change point problems and led to a significant body of literature in the field. The present monograph stands as a valuable contribution to this literature.

Key features and topics:

* Clear and systematic exposition with a great deal of introductory material included;

* Different models in each chapter, including gamma and exponential models, rarely examined thus far in the literature;

* Extensive examples to emphasize key concepts and different methodologies used, namely the likelihood ratio criterion as well as the Bayesian and information criterion approaches;

* An up-to-date comprehensive bibliography and two indices.

New to the Second Edition:

* New examples of change point analysis in modern molecular biology and other fields such as finance and air traffic control;

* Two new sections of applications of the underlying change point models in analyzing the array Comparative GenomicHybridization (aCGH) data for DNA copy number changes;

* A new chapter on change points in the hazard function;

* A new chapter on other practical change point models, such as the epidemic change point model and a smooth-and-abrupt change point model.

This monograph will be a highly useful resource for an impressively broad range of researchers in statistics, as well as a useful supplement for graduate courses in the field.