Copula as an effective way of modeling dependence has become more or less a standard tool in risk management, and a wide range of applications of copula models appear in the literature of economics, econometrics, insurance, finance, etc. How to estimate and test a copula plays an important role in practice, and both parametric and nonparametric methods have been studied in the literature. In th...

We propose a parametric version of Independent Component Analysis (ICA) via Copulas families of multivariate distributions that join univariate margins to multivariate distributions. Our procedure exploits the role for copula models in information theory and in measures of association, specifically: the use of copulae densities as parametric mutual information, and as measures of association on...

Vine copula provides a flexible tool to capture asymmetry in modelling multivariate distributions. Nevertheless, its flexibility is achieved at the expense of exponentially increasing complexity of the model. To alleviate this issue, the simplifying assumption (SA) is commonly adapted in specific applications of vine copula models. In this paper, generalized linear models (GLMs) are proposed fo...

In this paper, we consider nonparametric copula inference under bivariate censoring. Based on an estimator of the joint cumulative distribution function, we define a discrete and two smooth estimators of the copula. The construction that we propose is valid for a large number of estimators of the distribution function, and therefore for a large number of bivariate censoring frameworks. Under so...

DRAGAN RADULOVIĆ1, MARTEN WEGKAMP2 and YUE ZHAO3 1Department of Mathematics, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431, USA. E-mail: [email protected] 2Department of Mathematics and Department of Statistical Science, Cornell University, 432 Malott Hall, Ithaca, NY 14853, USA. E-mail: [email protected] 3Department of Statistical Science, Cornell University, 310 M...

In many practical situations, the dependence between the quantities is linear or approximately linear. Knowing that the dependence is linear simplifies computations; so, is is desirable to detect linear dependencies. If we know the joint probability distribution, we can detect linear dependence by computing Pearson’s correlation coefficient. In practice, we often have a copula instead of a full...

this study presents a new method for interpolation by use of copula for groundwater quality zoning. in this regard, the data of the concentration of bicarbonate in 87 piezometric wells on the plains of kerman and ravar in september 2013 were examined. for this purpose, four archimedean copula including clayton, frank, gumbel and joe have been used. then, the obtained results were compared to th...

The copula–entropy theory combines the entropy theory and the copula theory. The entropy theory has been extensively applied to derive the most probable univariate distribution subject to specified constraints by applying the principle of maximum entropy. With the flexibility to model nonlinear dependence structure, parametric copulas (e.g., Archimedean, extreme value, meta-elliptical, etc.) ha...

The univariate Piecing-Together approach (PT) fits a univariate generalized Pareto distribution (GPD) to the upper tail of a given distribution function (df) in a continuous manner. A multivariate extension was established by Aulbach et al. (2011a): The upper tail of a given copula C was cut off and substituted by the upper tail of a multivariate GPD-copula in a continuous manner. The result is...

Recent methods for estimating sparse undirected graphs for real-valued data in high dimensional problems rely heavily on the assumption of normality. We show how to use a semiparametric Gaussian copula—or “nonparanormal”—for high dimensional inference. Just as additive models extend linear models by replacing linear functions with a set of one-dimensional smooth functions, the nonparanormal ext...