Explores a variety of applications of dependence modeling with copulas Compares copula families and constructions with many kinds of dependence structures and tail properties Explains how the properties affect tail inferences, such as joint tail probabilities and tail conditional expectations Describes novel vine copula techniques for modeling high-dimensional data Covers inference, diagnostics, model selection, numerical methods, and algorithms for copula applications Includes some theoretical details and advanced examples Offers software and code on the author’s website Summary 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.
- ISBN: 9781466583221
- Editorial: CHAPMAN & HALL LTD.
- Encuadernacion: Tela
- Páginas: 480
- Fecha Publicación: 26/06/2014
- Nº Volúmenes: 1
- Idioma: