LEADER 04309nam 22006855 450 001 9910338251503321 005 20250402112708.0 010 $a3-030-13785-6 024 7 $a10.1007/978-3-030-13785-4 035 $a(CKB)4100000008160584 035 $a(DE-He213)978-3-030-13785-4 035 $a(MiAaPQ)EBC5923109 035 $a(PPN)23652240X 035 $a(EXLCZ)994100000008160584 100 $a20190514d2019 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAnalyzing Dependent Data with Vine Copulas $eA Practical Guide With R /$fby Claudia Czado 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (XXIX, 242 p. 70 illus., 25 illus. in color.) 225 1 $aLecture Notes in Statistics,$x2197-7186 ;$v222 311 08$a3-030-13784-8 327 $aPreface -- Multivariate Distributions and Copulas -- Dependence Measures -- Bivariate Copula Classes, Their Visualization and Estimation -- Pair Copula Decompositions and Constructions -- Regular Vines -- Simulating Regular Vine Copulas and Distributions -- Parameter Estimation in Regular Vine Copulas -- Selection of Regular Vine Copula Models -- Comparing Regular Vine Copula Models -- Case Study: Dependence Among German DAX Stocks -- Recent Developments in Vine Copula Based Modeling -- Indices. 330 $aThis textbook provides a step-by-step introduction to the class of vine copulas, their statistical inference and applications. It focuses on statistical estimation and selection methods for vine copulas in data applications. These flexible copula models can successfully accommodate any form of tail dependence and are vital to many applications in finance, insurance, hydrology, marketing, engineering, chemistry, aviation, climatology and health. The book explains the pair-copula construction principles underlying these statistical models and discusses how to perform model selection and inference. It also derives simulation algorithms and presents real-world examples to illustrate the methodological concepts. The book includes numerous exercises that facilitate and deepen readers? understanding, and demonstrates how the R package VineCopula can be used to explore and build statistical dependence models from scratch. In closing, the book provides insights into recent developments and open research questions in vine copula based modeling. The book is intended for students as well as statisticians, data analysts and any other quantitatively oriented researchers who are new to the field of vine copulas. Accordingly, it provides the necessary background in multivariate statistics and copula theory for exploratory data tools, so that readers only need a basic grasp of statistics and probability. 410 0$aLecture Notes in Statistics,$x2197-7186 ;$v222 606 $aStatistics 606 $aStatistics 606 $aBiometry 606 $aQuantitative research 606 $aMathematical statistics$xData processing 606 $aStatistical Theory and Methods 606 $aStatistics in Business, Management, Economics, Finance, Insurance 606 $aBiostatistics 606 $aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences 606 $aData Analysis and Big Data 606 $aStatistics and Computing 615 0$aStatistics. 615 0$aStatistics. 615 0$aBiometry. 615 0$aQuantitative research. 615 0$aMathematical statistics$xData processing. 615 14$aStatistical Theory and Methods. 615 24$aStatistics in Business, Management, Economics, Finance, Insurance. 615 24$aBiostatistics. 615 24$aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 615 24$aData Analysis and Big Data. 615 24$aStatistics and Computing. 676 $a519.535 700 $aCzado$b Claudia$4aut$4http://id.loc.gov/vocabulary/relators/aut$0780992 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910338251503321 996 $aAnalyzing Dependent Data with Vine Copulas$91668157 997 $aUNINA