| Description |
1 online resource (xviii, 462 pages) : illustrations, tables |
| Series |
Monographs on Statistics and Applied Probability ; 134 |
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Monographs on statistics and applied probability (Series) ; 134.
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| Contents |
A Chapter 1. Introduction -- Chapter 2. Basics: dependence, tail behavior and asymmetries -- Chapter 3. Copula construction methods -- Chapter 4. Parametric copula families and properties -- Chapter 5. Inference, diagnostics and model selection -- Chapter 6. Computing and algorithms -- Chapter 7. Applications and data examples -- Chapter 8. Theorems for properties of copulas -- Appendix A. Laplace transforms and Archimedean generators |
| Summary |
880-01 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 |
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880-01/(S Model comparisons Summary for inference Computing and Algorithms Roots of nonlinear equations Numerical optimization and maximum likelihood Numerical integration and quadrature Interpolation Numerical methods involving matrices Graphs and spanning trees Computation of τ, ρS, and ρN for copulas Computation of empirical Kendall's τSimulation from multivariate distributions and copulas Likelihood for vine copula Likelihood for factor copula Copula derivatives for factor and vine copulas Generation of vinesSimulation from vines and truncated vine models Partial correlations and vines Partial correlations and factor structure Searching for good truncated R-vine approximations Summary for algorithms Applications and Data Examples Data analysis with misspecified copula models Inferences on tail quantities Discretized multivariate Gaussian and R-vine approximation Insurance losses: bivariate continuous Longitudinal count: multivariate discrete Count time series Multivariate extreme values Multivariate financial returns Conservative tail inference Item response: multivariate ordinal SEM model as vine: alienation data SEM model as vine: attitude-behavior data Overview of applications Theorems for Properties of Copulas Absolutely continuous and singular components of multivariate distributions Continuity properties of copulas Dependence concepts Fréchet classes and compatibility Archimedean copulas Multivariate extreme value distributions Mixtures of max-id distributions Elliptical distributions Tail dependence Tail order Combinatorics of vines Vines and mixtures of conditional distributions Factor copulas Kendall functions Laplace transforms Regular variation Summary for further research Appendix: Laplace Transforms and Archimedean Generators Index |
| Notes |
"A Chapman & Hall book." |
| Bibliography |
Includes bibliographical references (pages 437-458) and index |
| Notes |
Print version record |
| Subject |
Copulas (Mathematical statistics)
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Dependence (Statistics)
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Probabilities.
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probability.
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MATHEMATICS -- Applied.
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MATHEMATICS -- Probability & Statistics -- General.
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Copulas (Mathematical statistics)
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Dependence (Statistics)
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Probabilities
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| Form |
Electronic book
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| LC no. |
2014018932 |
| ISBN |
9781466583238 |
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1466583231 |
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9781322635651 |
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132263565X |
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1466583223 |
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9781466583221 |
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9780429103186 |
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0429103182 |
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