03664nam a22005295i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001400172072001600186072002300202072001500225100008600240245010200326250001800428264006900446300006600515336002600581337002600607338003600633347002400669490004600693505028400739520142701023650003602450650003302486650002102519650002502540650003602565650003302601650002602634650007802660700008302738710003402821773002602855776003602881776003602917776003602953830005302989856004603042912001403088912001503102999001703117978-3-031-51609-2DE-He21320251030112036.0cr nn 008mamaa240511s2024    sz |    s    |||| 0|eng d  a97830315160929978-3-031-51609-27 a10.1007/978-3-031-51609-22doi 4aQA276-280 7aPBT2bicssc 7aMAT0290002bisacsh 7aPBT2thema1 aHorváth, Lajos.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut97471210aChange Point Analysis for Time Seriesh[electronic resource] /cby Lajos Horváth, Gregory Rice.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXIII, 545 p. 36 illus., 30 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aSpringer Series in Statistics,x2197-568X0 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.  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. 0aMathematical statistics.967233 0aTime-series analysis.976645 0aBiometry.965981 0aStatistics .96598214aMathematical Statistics.96723324aTime Series Analysis.97664624aBiostatistics.96598524aStatistics in Business, Management, Economics, Finance, Insurance.9697381 aRice, Gregory.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut9766472 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303151608508iPrinted edition:z978303151610808iPrinted edition:z9783031516115 0aSpringer Series in Statistics,x2197-568X97664840uhttps://doi.org/10.1007/978-3-031-51609-2  aZDB-2-SMA  aZDB-2-SXMS  c59774d59774