2011JSIAM LettersOpen access

Evolution of bivariate copulas in discrete processes

Yasukazu Yoshizawa, Naoyuki Ishimura

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Abstract

A copula function makes a bridge between multivariate joint distributions and univariate marginal distributions, and provides a flexible way of describing nonlinear dependence among random circumstances. We introduce a new family of bivariate copulas which evolves according to the discrete process of heat equation. We prove the convergence of solutions as well as the measure of dependence. Numerical experiments are also performed, which shows that our procedure works substantially well.

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A copula function makes a bridge between multivariate joint distributions and univariate marginal distributions, and provides a flexible way of describing nonlinear dependence among random circumstances. We introduce a new family of bivariate copulas which evolves according to the discrete process of heat equation. We prove the convergence of solutions as well as the measure of dependence. Numerical experiments are also performed, which shows that our procedure works substantially well.

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Available abstract

A copula function makes a bridge between multivariate joint distributions and univariate marginal distributions, and provides a flexible way of describing nonlinear dependence among random circumstances. We introduce a new family of bivariate copulas which evolves according to the discrete process of heat equation. We prove the convergence of solutions as well as the measure of dependence. Numerical experiments are also performed, which shows that our procedure works substantially well.

Key concepts: Bivariate analysis, Copula (linguistics), Univariate, Mathematics, Marginal distribution, Multivariate statistics, Joint probability distribution, Applied mathematics

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