Displaying similar documents to “Semiparametric estimation of the parameters of multivariate copulas”

Nonparametric estimation of simplified vine copula models: comparison of methods

Thomas Nagler, Christian Schellhase, Claudia Czado (2017)

Dependence Modeling

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In the last decade, simplified vine copula models have been an active area of research. They build a high dimensional probability density from the product of marginals densities and bivariate copula densities. Besides parametric models, several approaches to nonparametric estimation of vine copulas have been proposed. In this article, we extend these approaches and compare them in an extensive simulation study and a real data application. We identify several factors driving the relative...

New estimates and tests of independence in semiparametric copula models

Salim Bouzebda, Amor Keziou (2010)

Kybernetika

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We introduce new estimates and tests of independence in copula models with unknown margins using φ -divergences and the duality technique. The asymptotic laws of the estimates and the test statistics are established both when the parameter is an interior or a boundary value of the parameter space. Simulation results show that the choice of χ 2 -divergence has good properties in terms of efficiency-robustness.

Multivariate probability integral transformation: application to maximum likelihood estimation.

Abderrahmane Chakak, Layachi Imlahi (2001)

RACSAM

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Sea (X, X) un vector aleatorio con una función de distribución F. La transformación integral de la probabilidad (pit) es la variable aleatoria unidimensional P = F(X, X). La expresion de su función de distribución, y un algoritmo de simulación en términos de la función cuantil, dada por Chakak et al [2000], cuando la distribución es absolumente continua, son extendidas a distribuciones que pueden tener singularidades. La estimación de máxima verosimilitud del parámetro de dependencia...

Quantifying the impact of different copulas in a generalized CreditRisk + framework An empirical study

Kevin Jakob, Matthias Fischer (2014)

Dependence Modeling

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Without any doubt, credit risk is one of the most important risk types in the classical banking industry. Consequently, banks are required by supervisory audits to allocate economic capital to cover unexpected future credit losses. Typically, the amount of economical capital is determined with a credit portfolio model, e.g. using the popular CreditRisk+ framework (1997) or one of its recent generalizations (e.g. [8] or [15]). Relying on specific distributional assumptions, the credit...

Estimation and tests in finite mixture models of nonparametric densities

Odile Pons (2009)

ESAIM: Probability and Statistics

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The aim is to study the asymptotic behavior of estimators and tests for the components of identifiable finite mixture models of nonparametric densities with a known number of components. Conditions for identifiability of the mixture components and convergence of identifiable parameters are given. The consistency and weak convergence of the identifiable parameters and test statistics are presented for several models.

On Truncation Invariant Copulas and their Estimation

Piotr Jaworski (2017)

Dependence Modeling

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The paper deals with the family of irreducible left truncation invariant bivariate copulas, which admit a nontrivial lower tail dependence function. Such copulas, similarly as the Archimedean ones, are characterized by a functional parameter, a generator being an increasing convex function.We provide a nonparametric, piece-wise linear estimator of such generators.