International audienceThis paper deals with the problem of the multivariate copula density estimation. Using wavelet methods we provide two shrinkage procedures based on thresholding rules for which the knowledge of the regularity of the copula density to be estimated is not necessary. These methods, said to be adaptive, are proved to perform very well when adopting the minimax and the maxiset approaches. Moreover we show that these procedures can be discriminated in the maxiset sense. We produce an estimation algorithm whose qualities are evaluated thanks some simulation. Last, we propose a real life application for financial data
The objective of this paper is to estimate a bivariate density nonparametrically from a dataset from...
In this research we introduce a new class of multivariate probability models to the marketing litera...
Statistical and machine learning is a fundamental task in sensor networks. Real world data almost al...
International audienceThis paper deals with the problem of the multivariate copula density estimatio...
AbstractThis paper deals with the problem of multivariate copula density estimation. Using wavelet m...
Wavelet analysis is used to construct a rank-based estimator of a copula density. The procedure, whi...
A copula density is the joint probability density function (PDF) of a random vector with uniform mar...
International audienceThis paper attempts to better understand the influence of the smoothness of th...
Today, we will go further on the inference of copula functions. Some codes (and references) can be f...
Exposé aux Gemeinsame Jahrestagung der Deutschen Mathematiker-Vereinigung und der Gesellschaft für D...
The purpose of this paper is twofold: Fisrt, we review briefly the methods often used for copula est...
42 pages, 6 figures, 9 tablesIn this paper we study nonparametric estimators of copulas and copula d...
A fundamental problem in statistics is the estimation of dependence between random variables. While ...
International audienceIn this paper, we propose simple estimation methods dedicated to a semiparamet...
Copulas are full measures of dependence among random variables. They are increasingly popular among...
The objective of this paper is to estimate a bivariate density nonparametrically from a dataset from...
In this research we introduce a new class of multivariate probability models to the marketing litera...
Statistical and machine learning is a fundamental task in sensor networks. Real world data almost al...
International audienceThis paper deals with the problem of the multivariate copula density estimatio...
AbstractThis paper deals with the problem of multivariate copula density estimation. Using wavelet m...
Wavelet analysis is used to construct a rank-based estimator of a copula density. The procedure, whi...
A copula density is the joint probability density function (PDF) of a random vector with uniform mar...
International audienceThis paper attempts to better understand the influence of the smoothness of th...
Today, we will go further on the inference of copula functions. Some codes (and references) can be f...
Exposé aux Gemeinsame Jahrestagung der Deutschen Mathematiker-Vereinigung und der Gesellschaft für D...
The purpose of this paper is twofold: Fisrt, we review briefly the methods often used for copula est...
42 pages, 6 figures, 9 tablesIn this paper we study nonparametric estimators of copulas and copula d...
A fundamental problem in statistics is the estimation of dependence between random variables. While ...
International audienceIn this paper, we propose simple estimation methods dedicated to a semiparamet...
Copulas are full measures of dependence among random variables. They are increasingly popular among...
The objective of this paper is to estimate a bivariate density nonparametrically from a dataset from...
In this research we introduce a new class of multivariate probability models to the marketing litera...
Statistical and machine learning is a fundamental task in sensor networks. Real world data almost al...