Analyzing Dependent Data with Vine Copulas - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Analyzing Dependent Data with Vine Copulas write by Claudia Czado. This book was released on 2019-05-14. Analyzing Dependent Data with Vine Copulas available in PDF, EPUB and Kindle. This 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.
Dependence Modeling
Dependence Modeling - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Dependence Modeling write by Harry Joe. This book was released on 2011. Dependence Modeling available in PDF, EPUB and Kindle. 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. Gronneberg -- 7. Dependence comparisons of vine copulae with four or more variables / H. Joe -- 8. Tail dependence in vine copulae / H. Joe -- 9. 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. Gartner and A. Min -- 14. Non-parametric Bayesian belief nets versus vines / A. Hanea -- 15. Modeling dependence between financial returns using pair-copula constructions / K. Aas and D. Berg -- 16. Dynamic D-vine model / A. Heinen and A. Valdesogo -- 17. Summary and future directions / D. Kurowicka
Dependence Modeling with Copulas
Dependence Modeling with Copulas - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Dependence Modeling with Copulas write by Harry Joe. This book was released on 2014-06-26. Dependence Modeling with Copulas available in PDF, EPUB and Kindle. Dependence Modeling with Copulas covers the substantial advances that have taken place in the field during the last 15 years, including vine copula modeling of high-dimensional data. Vine copula models are constructed from a sequence of bivariate copulas. The book develops generalizations of vine copula models, including common and structured factor models that extend from the Gaussian assumption to copulas. It also discusses other multivariate constructions and parametric copula families that have different tail properties and presents extensive material on dependence and tail properties to assist in copula model selection. The author shows how numerical methods and algorithms for inference and simulation are important in high-dimensional copula applications. He presents the algorithms as pseudocode, illustrating their implementation for high-dimensional copula models. He also incorporates results to determine dependence and tail properties of multivariate distributions for future constructions of copula models.
Dependence Modeling
Dependence Modeling - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Dependence Modeling write by Harry Joe. This book was released on 2011. Dependence Modeling available in PDF, EPUB and Kindle. 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. Gronneberg -- 7. Dependence comparisons of vine copulae with four or more variables / H. Joe -- 8. Tail dependence in vine copulae / H. Joe -- 9. 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. Gartner and A. Min -- 14. Non-parametric Bayesian belief nets versus vines / A. Hanea -- 15. Modeling dependence between financial returns using pair-copula constructions / K. Aas and D. Berg -- 16. Dynamic D-vine model / A. Heinen and A. Valdesogo -- 17. Summary and future directions / D. Kurowicka
Multivariate Frequency Analysis of Hydro-Meteorological Variables
Multivariate Frequency Analysis of Hydro-Meteorological Variables - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Multivariate Frequency Analysis of Hydro-Meteorological Variables write by Fateh Chebana. This book was released on 2022-11-16. Multivariate Frequency Analysis of Hydro-Meteorological Variables available in PDF, EPUB and Kindle. Multivariate Frequency Analysis of Hydro-Meteorological Variables: A Copula-Based Approach provides comprehensive and detailed descriptions of the approaches and techniques used in multivariate frequency analysis (including, but not limited to copula functions), with illustrative examples and real-life case studies provided. The book presents all background material and new developments in one place, presenting the material in a homogeneous and pedagogical way in order to allow students, engineers and researchers to access and efficiently use all information surrounding this topic. This reference can be used as a guide to apply the available and recent approaches to evaluate hydro-meteorological risks, to design hydraulic structures, in teaching (faculty members), and as a literature review to go to the next steps in research projects (graduate students and postdocs). Presents methods for analysis of hydro-meteorological risks followed by illustrative examples based on real life data sets Provides definitions throughout on all new topics and key terms Includes case studies and real-life examples covering a variety of situations and showing how this work can be applied in the reader’s own work
