Principles of Copula Theory

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A01=Carlo Sempi
A01=Fabrizio Durante
A1i1 A1i1
advanced dependence structures in mathematics
application of copulas
Archimedean Copulas
Archimedean T-norms
Author_Carlo Sempi
Author_Fabrizio Durante
bivariate
Bivariate Copula
borel
Borel Set
Category=KCH
Category=PBT
Category=PBW
Conditional Expectation
Copulas and measure theory
copulas and stochastic dependence
d-dimensional copulas
Dini Derivatives
Distribution Function
eq_bestseller
eq_business-finance-law
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
families of copulas
function
generalizations of copulas
Hausdorff Dimension
Kendall's Tau
Kendall’s Tau
Left Inverse
Marginal Survival Functions
Markov Kernel
Markov Operators
Markov product for 2-copulas
Markov product methods
Marshall Olkin Copulas
measure
measure theory applications
probability
Probability Space
Probability Survival Function
quasi-copulas
quasi-copulas theory
random
Random Vector
random vector analysis
random vector using copulas
Regular Conditional Distribution
semi-copulas
semi-copulas properties
set
shuffles of copulas
Sklar's Theorem
Sklar’s Theorem
space
stochastic dependence modeling
survival
Survival Function
Tail Dependence
TDCs
Triangular Norms
Univariate Marginals
vectors

Product details

  • ISBN 9781032098470
  • Weight: 476g
  • Dimensions: 156 x 234mm
  • Publication Date: 30 Jun 2021
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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Principles of Copula Theory explores the state of the art on copulas and provides you with the foundation to use copulas in a variety of applications. Throughout the book, historical remarks and further readings highlight active research in the field, including new results, streamlined presentations, and new proofs of old results.

After covering the essentials of copula theory, the book addresses the issue of modeling dependence among components of a random vector using copulas. It then presents copulas from the point of view of measure theory, compares methods for the approximation of copulas, and discusses the Markov product for 2-copulas. The authors also examine selected families of copulas that possess appealing features from both theoretical and applied viewpoints. The book concludes with in-depth discussions on two generalizations of copulas: quasi- and semi-copulas.

Although copulas are not the solution to all stochastic problems, they are an indispensable tool for understanding several problems about stochastic dependence. This book gives you the solid and formal mathematical background to apply copulas to a range of mathematical areas, such as probability, real analysis, measure theory, and algebraic structures.

Fabrizio Durante is a professor in the Faculty of Economics and Management at the Free University of Bozen–Bolzano. He is an associate editor of Computational Statistics & Data Analysis and Dependence Modeling. His research focuses on multivariate dependence models with copulas, reliability theory and survival analysis, and quantitative risk management. He earned a PhD in mathematics from the University of Lecce and habilitation in mathematics from the Johannes Kepler University Linz.

Carlo Sempi is a professor in the Department of Mathematics and Physics at the University of Salento. He has published nearly 100 articles in many journals. His research interests include copulas, quasi-copulas, semi-copulas, weak convergence, metric spaces, and normed spaces. He earned a PhD in applied mathematics from the University of Waterloo.

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