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010   $a3031516095
010   $a9783031516092
010   $z3031516087$b(hardcover)
010   $z9783031516085$b(hardcover)
024 7 $a10.1007/978-3-031-51609-2
035   $a(CKB)32027833500041
035   $a(MiAaPQ)EBC31342650
035   $a(Au-PeEL)EBL31342650
035   $a(MiAaPQ)EBC31338533
035   $a(Au-PeEL)EBL31338533
035   $a(DE-He213)978-3-031-51609-2
035   $a(EXLCZ)9932027833500041
100   $a20240511d2024      u|         0
101 0 $aeng
135   $aur|n#|||a|||a
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aChange Point Analysis for Time Series /$fby Lajos Horváth, Gregory Rice
205   $a1st ed. 2024.
210  1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024.
215   $a1 online resource (545 pages)
225 1 $aSpringer Series in Statistics,$x2197-568X
311 08$a3-031-51608-7 
320   $aIncludes bibliographical references.
327   $aCumulative 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.
330   $aThis volume provides a comprehensive survey that covers various modern methods used for detecting and estimating change points in time series and their models. The book primarily focuses on asymptotic theory and practical applications of change point analysis. The methods discussed in the book go beyond the traditional change point methods for univariate and multivariate series. It also explores techniques for handling heteroscedastic series, high-dimensional series, and functional data. While the primary emphasis is on retrospective change point analysis, the book also presents sequential "on-line" methods for detecting change points in real-time scenarios. Each chapter in the book includes multiple data examples that illustrate the practical application of the developed results. These examples cover diverse fields such as economics, finance, environmental studies, and health data analysis. To reinforce the understanding of the material, each chapter concludes with several exercises. Additionally, the book provides a discussion of background literature, allowing readers to explore further resources for in-depth knowledge on specific topics. Overall, "Change Point Analysis for Time Series" offers a broad and informative overview of modern methods in change point analysis, making it a valuable resource for researchers, practitioners, and students interested in analyzing and modeling time series data.
410  0$aSpringer Series in Statistics,$x2197-568X
606   $aMathematical statistics
606   $aTime-series analysis
606   $aBiometry
606   $aStatistics
606   $aMathematical Statistics
606   $aTime Series Analysis
606   $aBiostatistics
606   $aStatistics in Business, Management, Economics, Finance, Insurance
615  0$aMathematical statistics.
615  0$aTime-series analysis.
615  0$aBiometry.
615  0$aStatistics.
615 14$aMathematical Statistics.
615 24$aTime Series Analysis.
615 24$aBiostatistics.
615 24$aStatistics in Business, Management, Economics, Finance, Insurance.
676   $a519.23
700   $aHorva?th$b Lajos$f1956-$0736447
702   $aRice$b Greg
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910857791803321
996   $aChange Point Analysis for Time Series$94161930
997   $aUNINA