Dependence Modeling with Copulas
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.

1128917670
Dependence Modeling with Copulas
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.

125.0 In Stock
Dependence Modeling with Copulas

Dependence Modeling with Copulas

by Harry Joe
Dependence Modeling with Copulas
Add to Wishlist
Dependence Modeling with Copulas

Dependence Modeling with Copulas

by Harry Joe

Overview

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.

Product Details

ISBN-13: 9781466583221
Publisher: Taylor & Francis
Publication date: 06/26/2014
Series: Chapman & Hall/CRC Monographs on Statistics and Applied Probability , #133
Edition description: New Edition
Pages: 480
Product dimensions: 7.30(w) x 10.00(h) x 0.60(d)

Table of Contents

Introduction. Basics: Dependence, Tail Behavior, and Asymmetries. Copula Construction Methods. Parametric Copula Families and Properties. Inference, Diagnostics, and Model Selection. Computing and Algorithms. Applications and Data Examples. Theorems for Properties of Copulas. Appendix. Index.

From the B&N Reads Blog
Page 1 of

Related Subjects

Science & Technology
Mathematics
Probability Theory
Statistics
Science & Technology
Mathematics
Probability Theory
Statistics

Customer Reviews