03877nam a22005775i 4500 978-3-031-51609-2 DE-He213 20251030112036.0 cr nn 008mamaa 240511s2024 sz | s |||| 0|eng d 9783031516092 978-3-031-51609-2 10.1007/978-3-031-51609-2 doi QA276-280 PBT bicssc MAT029000 bisacsh PBT thema Horváth, Lajos. author. aut http://id.loc.gov/vocabulary/relators/aut 74712 Change Point Analysis for Time Series [electronic resource] / by Lajos Horváth, Gregory Rice. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XIII, 545 p. 36 illus., 30 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Springer Series in Statistics, 2197-568X 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. Mathematical statistics. 67233 Time-series analysis. 76645 Biometry. 65981 Statistics . 65982 Mathematical Statistics. 67233 Time Series Analysis. 76646 Biostatistics. 65985 Statistics in Business, Management, Economics, Finance, Insurance. 69738 Rice, Gregory. author. aut http://id.loc.gov/vocabulary/relators/aut 76647 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031516085 Printed edition: 9783031516108 Printed edition: 9783031516115 Springer Series in Statistics, 2197-568X 76648 https://doi.org/10.1007/978-3-031-51609-2 ZDB-2-SMA ZDB-2-SXMS 59774 59774