Nonparametric estimation of copula functions for dependence modelling
نویسندگان
چکیده
منابع مشابه
Nonparametric estimation of copula functions for dependence modelling
Copulas are full measures of dependence among components of random vectors. Unlike the marginal and the joint distributions, which are directly observable, a copula is a hidden dependence structure that couples a joint distribution with its marginals. This makes the task of proposing a parametric copula model non-trivial and is where a nonparametric estimator can play a significant role. In thi...
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The tail copula is widely used to describe the dependence in the tail of multivariate distributions. In some situations such as risk management, the dependence structure may be linked with some covariate. The tail copula thus depends on this covariate and is referred to as the conditional tail copula. The aim of this paper is to propose a nonparametric estimator of the conditional tail copula a...
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We propose a new framework for dependence structure learning via copula. Copula is a statistical theory on dependence and measurement of association. Graphical models are considered as a type of special case of copula families, named product copula. In this paper, a nonparametric algorithm for copula estimation is presented. Then a Chow-Liu like method based on dependence measure via copula is ...
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ژورنال
عنوان ژورنال: Canadian Journal of Statistics
سال: 2007
ISSN: 0319-5724,1708-945X
DOI: 10.1002/cjs.5550350205