[go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Change Point Analysis for Time Series

  • Book
  • © 2024

Overview

Authors:
  1. Lajos Horváth

    1. Department of Mathematics, University of Utah, Salt Lake City, USA

    View author publications

    You can also search for this author in PubMed   Google Scholar

  2. Gregory Rice

    1. Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada

    View author publications

    You can also search for this author in PubMed   Google Scholar

  • Provides a comprehensive review of asymptotic methods in change point analysis for time series
  • Extends classical change point methods to the modern settings of high--dimensional, functional, and heteroscedastic data
  • Illustrated through real applications to health, environmental, and econometric data sets

Part of the book series: Springer Series in Statistics (SSS)

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

eBook USD 119.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

About this book

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.

Keywords

Table of contents (8 chapters)

  1. Front Matter

    Pages i-xiii
    Download chapter PDF

  2. Cumulative Sum Processes

    • Lajos Horváth, Gregory Rice
    Pages 1-28

  3. Change Point Analysis of the Mean

    • Lajos Horváth, Gregory Rice
    Pages 29-87

  4. Variance Estimation, Change Points in Variance, and Heteroscedasticity

    • Lajos Horváth, Gregory Rice
    Pages 89-144

  5. Regression Models

    • Lajos Horváth, Gregory Rice
    Pages 145-205

  6. Parameter Changes in Time Series Models

    • Lajos Horváth, Gregory Rice
    Pages 207-324

  7. Sequential Monitoring

    • Lajos Horváth, Gregory Rice
    Pages 325-363

  8. High-Dimensional and Panel Data

    • Lajos Horváth, Gregory Rice
    Pages 365-420

  9. Functional Data

    • Lajos Horváth, Gregory Rice
    Pages 421-499

  10. Back Matter

    Pages 501-545
    Download chapter PDF
Back to top

Authors and Affiliations

  • Department of Mathematics, University of Utah, Salt Lake City, USA

    Lajos Horváth

  • Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada

    Gregory Rice

About the authors

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 theareas 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.

Bibliographic Information

Publish with us

Policies and ethics

Back to top