Elements of Copula Modeling with R
Authors: Hofert, M., Kojadinovic, I., Mächler, M., Yan, J.
Free Preview Offers an introduction to copulas and their main properties, along with the most important theoretical results
 Illustrates the concepts using standalone and reproducible R examples involving synthetic or realworld data
 Elaborates copula transformations, copula estimation, graphical diagnostics, statistical tests and model selection
 Addresses advanced topics such as the handling of ties, time series and covariates in a regression setting
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 About this book

This book introduces the main theoretical findings related to copulas and shows how statistical modeling of multivariate continuous distributions using copulas can be carried out in the R statistical environment with the package copula (among others).
Copulas are multivariate distribution functions with standard uniform univariate margins. They are increasingly applied to modeling dependence among random variables in fields such as risk management, actuarial science, insurance, finance, engineering, hydrology, climatology, and meteorology, to name a few.
In the spirit of the Use R! series, each chapter combines key theoretical definitions or results with illustrations in R. Aimed at statisticians, actuaries, risk managers, engineers and environmental scientists wanting to learn about the theory and practice of copula modeling using R without an overwhelming amount of mathematics, the book can also be used for teaching a course on copula modeling.
 About the authors

The four authors of the book are the authors of the R package copula available on CRAN.
Marius Hofert is an assistant professor of statistics at the University of Waterloo, Canada. He obtained his Ph.D. in mathematics from the University of Ulm, Germany in 2010. He then held a postdoctoral research position at ETH Zurich, Switzerland. After guest assistant professorships at the Technical University Munich, Germany and the University of Washington, USA, he joined the Department of Statistics and Actuarial Science at the University of Waterloo in 2014. His main research interests lie in copula modeling, computational statistics, data science and quantitative risk management.
Ivan Kojadinovic is a professor of statistics at the University of Pau, France. He received his Ph.D. from the University of Reunion, France in 2002 and joined the University of Nantes, France in 2003 as an assistant professor. From 2007 to 2010, he was a lecturer and then a senior lecturer at the Department of Statistics of the University of Auckland, New Zealand, before joining the University of Pau in 2010. His research interests lie in nonparametric statistics, copulas, changepoint detection, and environmental and financial applications.
Martin Mächler is a lecturer and senior scientist at the ETH Zurich, Switzerland. He received his Ph.D. in mathematics from the ETH in 1989, and spent his postdoc years at the University of Washington, Seattle and Bell Communications Research (Bellcore), before joining the Seminar für Statistik at the ETH as lecturer in 1991. He became involved with R in 1995, was a founding member of the R core team in 1997 and has since been active in the development of R. His research interests include nonparametric curve estimation, numerical approximation, clustering, robust statistics, sparse matrices and statistical computing in general. He has been the maintainer of circa 20 CRAN R packages, including the "recommended" packages Matrix and cluster.
Jun Yan is a professor of statistics at the University of Connecticut, USA. He received his Ph.D. in statistics from University of Wisconsin  Madison, USA in 2003. He was an assistant professor at the University of Iowa, USA before joining UConn in 2007. His research interests include survival analysis, clustered data analysis, multivariate dependence, spatial extremes, and statistical computing. Actively involved in collaborative research in public health and environmental sciences, he has a special interest in making advanced statistical methods widely accessible via open source software.
 Table of contents (6 chapters)


Introduction
Pages 18

Copulas
Pages 979

Classes and Families
Pages 81132

Estimation
Pages 133165

Graphical Diagnostics, Tests, and Model Selection
Pages 167196

Table of contents (6 chapters)
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Bibliographic Information
 Bibliographic Information

 Book Title
 Elements of Copula Modeling with R
 Authors

 Marius Hofert
 Ivan Kojadinovic
 Martin Mächler
 Jun Yan
 Series Title
 Use R!
 Copyright
 2018
 Publisher
 Springer International Publishing
 Copyright Holder
 Springer International Publishing AG, part of Springer Nature
 eBook ISBN
 9783319896359
 DOI
 10.1007/9783319896359
 Softcover ISBN
 9783319896342
 Series ISSN
 21975736
 Edition Number
 1
 Number of Pages
 X, 267
 Number of Illustrations
 576 b/w illustrations, 21 illustrations in colour
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