Change Point Analysis for Time Series

This volume provides a comprehensive survey that covers various modern methods used for detecting and estimating change points in time series and their models. The book primarily focuses on asymptotic theory and practical applications of change point analysis. The methods discussed in the book go beyond the traditional change point methods for univariate and multivariate series. It also explores techniques for handling heteroscedastic series, high-dimensional series, and functional data. While the primary emphasis is on retrospective change point analysis, the book also presents sequential ＂on-line＂ methods for detecting change points in real-time scenarios. Each chapter in the book includes multiple data examples that illustrate the practical application of the developed results. These examples cover diverse fields such as economics, finance, environmental studies, and health data analysis. To reinforce the understanding of the material, each chapter concludes with several exercises.Additionally, the book provides a discussion of background literature, allowing readers to explore further resources for in-depth knowledge on specific topics. Overall, ＂Change Point Analysis for Time Series＂ offers a broad and informative overview of modern methods in change point analysis, making it a valuable resource for researchers, practitioners, and students interested in analyzing and modeling time series data.

Lajos Horváth is a faculty member in the Department of Mathematics at the University of Utah. He has coauthored over 300 peer reviewed papers and 5 books in the areas of statistics and probability on the topics of empirical process theory, functional data analysis, and change point analysis. He became a fellow at the Institute of Mathematical Statistics in 1990. He has been acknowledged as an ISI highly cited researcher. In addition to his research, Lajos has played significant editorial roles in several top research journals, including Statistics & Probability Letters, Journal of Statistical Planning and Inference and Journal of Time Series Econometrics.
Gregory Rice is a faculty member in the Department of Statistics and Actuarial Science at the University of Waterloo. He received his undergraduate degree in mathematics from Oregon State University, and a PhD in mathematics from the University of Utah. He has coauthored over 40 papers in the areas of functional data and time series analysis. His work has been supported by the Natural Science and Engineering Research Council of Canada Discovery Accelerator program.