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Change point analysis for time series

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Change point analysis for time seriesby Lajos Horváth, Gregory Rice.
作者:	Horváth, Lajos.
其他作者:	Rice, Gregory.
出版者:	Cham :Springer Nature Switzerland :2024.
面頁冊數:	xiii, 545 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Change-point problems.
電子資源:	https://doi.org/10.1007/978-3-031-51609-2
ISBN:	9783031516092$q(electronic bk.)

Change point analysis for time series
Horváth, Lajos.

Change point analysis for time series[electronic resource] /by Lajos Horváth, Gregory Rice. - Cham :Springer Nature Switzerland :2024. - xiii, 545 p. :ill. (some col.), digital ;24 cm. - Springer series in statistics,2197-568X. - Springer series in statistics..

Cumulative Sum Processes -- Change Point Analysis of the Mean -- Variance Estimation, Change Points in Variance, and Heteroscedasticity -- Regression Models -- Parameter Changes in Time Series Models -- Sequential Monitoring -- High-dimensional and Panel Data -- Functional Data.

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.

ISBN: 9783031516092$q(electronic bk.)

Standard No.: 10.1007/978-3-031-51609-2doiSubjects--Topical Terms:

182694
Change-point problems.

LC Class. No.: QA276

Dewey Class. No.: 519.5

Change point analysis for time series
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520 $a 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.
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