<電子ブック>
Change Point Analysis for Time Series / by Lajos Horváth, Gregory Rice
(Springer Series in Statistics. ISSN:2197568X)
版 | 1st ed. 2024. |
---|---|
出版者 | (Cham : Springer Nature Switzerland : Imprint: Springer) |
出版年 | 2024 |
本文言語 | 英語 |
大きさ | XIII, 545 p. 36 illus., 30 illus. in color : online resource |
著者標目 | *Horváth, Lajos author Rice, Gregory author SpringerLink (Online service) |
件 名 | LCSH:Mathematical statistics LCSH:Time-series analysis LCSH:Biometry LCSH:Statistics FREE:Mathematical Statistics FREE:Time Series Analysis FREE:Biostatistics FREE:Statistics in Business, Management, Economics, Finance, Insurance |
一般注記 | 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 HTTP:URL=https://doi.org/10.1007/978-3-031-51609-2 |
目次/あらすじ
所蔵情報を非表示
電子ブック | 配架場所 | 資料種別 | 巻 次 | 請求記号 | 状 態 | 予約 | コメント | ISBN | 刷 年 | 利用注記 | 指定図書 | 登録番号 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
電子ブック | オンライン | 電子ブック |
|
Springer eBooks | 9783031516092 |
|
電子リソース |
|
EB00238269 |