书目名称 | Change Point Analysis for Time Series |
编辑 | Lajos Horváth,Gregory Rice |
视频video | http://file.papertrans.cn/224/223708/223708.mp4 |
概述 | 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 heter |
丛书名称 | Springer Series in Statistics |
图书封面 |  |
描述 | 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 |
出版日期 | Book 2024 |
关键词 | Change Point Analysis; Time Series; ARMA; GARCH; Dynamic Linear Models; Heteroscedastic Time Series; Funct |
版次 | 1 |
doi | https://doi.org/10.1007/978-3-031-51609-2 |
isbn_softcover | 978-3-031-51611-5 |
isbn_ebook | 978-3-031-51609-2Series ISSN 0172-7397 Series E-ISSN 2197-568X |
issn_series | 0172-7397 |
copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |