Overview
- Offers a step-by-step introduction to vine copula based dependence modeling, including model selection and estimation methods for vine copulas
- Features easy-to-follow derivations and illustrations to explain the underlying statistical concepts
- Includes numerous exercises and real-world examples, and shows how to use the R package VineCopula to explore, build and compare vine based models
- Provides an overview of the latest developments in vine copula modeling
Part of the book series: Lecture Notes in Statistics (LNS, volume 222)
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Table of contents (11 chapters)
Keywords
- vine copulas
- dependence modelling
- copulas
- tail dependence
- multivariate statistics
- model selection
- R package VineCopula
- statistical inference for vine copulas
- dependent data
- dependence measures
- bivariate copula
- pair copula
- regular vine copula
- vine copula based modeling
- case study
- parameter estimation in copulas
- pair copula decomposition
- simulating regular vine copulas
About this book
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.
Authors and Affiliations
About the author
Claudia Czado is an Associate Professor of Applied Mathematical Statistics at the Technical University of Munich, Germany. Her research interests are in the dependence modeling of complex data structures, copula based quantile regression, generalized linear models and computational Bayesian methods, and the applications of these methods. She holds a Ph.D. in Operations Research and Industrial Engineering from Cornell University, USA.
Bibliographic Information
Book Title: Analyzing Dependent Data with Vine Copulas
Book Subtitle: A Practical Guide With R
Authors: Claudia Czado
Series Title: Lecture Notes in Statistics
DOI: https://doi.org/10.1007/978-3-030-13785-4
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Softcover ISBN: 978-3-030-13784-7Published: 15 May 2019
eBook ISBN: 978-3-030-13785-4Published: 14 May 2019
Series ISSN: 0930-0325
Series E-ISSN: 2197-7186
Edition Number: 1
Number of Pages: XXIX, 242
Number of Illustrations: 45 b/w illustrations, 25 illustrations in colour
Topics: Statistical Theory and Methods, Statistics for Business, Management, Economics, Finance, Insurance, Statistics for Life Sciences, Medicine, Health Sciences, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Big Data/Analytics, Statistics and Computing/Statistics Programs