03774nam 2200637 a 450 991078855520332120230725045527.01-283-14441-79786613144416981-4299-88-X(CKB)3360000000001365(EBL)731162(OCoLC)741492811(SSID)ssj0000634096(PQKBManifestationID)12207111(PQKBTitleCode)TC0000634096(PQKBWorkID)10622242(PQKB)11008330(MiAaPQ)EBC731162(WSP)00007699(Au-PeEL)EBL731162(CaPaEBR)ebr10480285(CaONFJC)MIL314441(EXLCZ)99336000000000136520110712d2011 uy 0engur|n|---|||||txtccrDependence modeling[electronic resource] vine copula handbook /editors, Dorota Kurowicka, Harry JoeHackensack, N.J. World Scientific20111 online resource (368 p.)Description based upon print version of record.981-4299-87-1 Includes bibliographical references and index.Preface; Contents; 1. Introduction: Dependence Modeling D. Kurowicka; 2. Multivariate Copulae M. Fischer; 3. Vines Arise R. M. Cooke, H. Joe and K. Aas; 4. Sampling Count Variables with Specified Pearson Correlation: A Comparison between a Naive and a C-Vine Sampling Approach V. Erhardt and C. Czado; 5. Micro Correlations and Tail Dependence R. M. Cooke, C. Kousky and H. Joe; 6. The Copula Information Criterion and Its Implications for the Maximum Pseudo-Likelihood Estimator S. Grønneberg; 7. Dependence Comparisons of Vine Copulae with Four or More Variables H. Joe8. Tail Dependence in Vine Copulae H. Joe9. Counting Vines O. Morales-Napoles; 10. Regular Vines: Generation Algorithm and Number of Equivalence Classes H. Joe, R. M. Cooke and D. Kurowicka; 11. Optimal Truncation of Vines D. Kurowicka; 12. Bayesian Inference for D-Vines: Estimation and Model Selection C. Czado and A. Min; 13. Analysis of Australian Electricity Loads Using Joint Bayesian Inference of D-Vines with Autoregressive Margins C. Czado, F. G ̈artner and A. Min; 14. Non-Parametric Bayesian Belief Nets versus Vines A. Hanea15. Modeling Dependence between Financial Returns Using Pair-Copula Constructions K. Aas and D. Berg16. Dynamic D-Vine Model A. Heinen and A. Valdesogo; 17. Summary and Future Directions D. Kurowicka; IndexThis book is a collaborative effort from three workshops held over the last three years, all involving principal contributors to the vine-copula methodology. Research and applications in vines have been growing rapidly and there is now a growing need to collate basic results, and standardize terminology and methods. Specifically, this handbook will trace historical developments, standardizing notation and terminology, summarize results on bivariate copulae, summarize results for regular vines, and give an overview of its applications. In addition, many of these results are new and not readily Copulas (Mathematical statistics)Dependence (Statistics)Distribution (Probability theory)Copulas (Mathematical statistics)Dependence (Statistics)Distribution (Probability theory)519.5Kurowicka Dorota474601Joe Harry411519MiAaPQMiAaPQMiAaPQBOOK9910788555203321Dependence modeling3676432UNINA