Information Measures via Copula Functions | Zendy

R. Mohtashami Borzadaran | Zendy; Mohammad Amini | Zendy

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Information Measures via Copula Functions

Author(s) -

R. Mohtashami Borzadaran,

Mohammad Amini

Publication year - 2010

Publication title -

journal of statistical research of iran

Language(s) - English

Resource type - Journals

ISSN - 1735-1294

DOI - 10.18869/acadpub.jsri.7.1.47

Subject(s) - hellinger distance , copula (linguistics) , mathematics , kullback–leibler divergence , divergence (linguistics) , parametric statistics , inference , differential entropy , nonparametric statistics , statistics , econometrics , computer science , artificial intelligence , joint entropy , principle of maximum entropy , philosophy , linguistics

In applications of differential geometry to problems of parametric inference, the notion of divergence is often used to measure the separation between two parametric densities. Among them, in this paper, we will verify measures such as Kullback-Leibler information, J-divergence, Hellinger distance, α-Divergence, . . . and so on. Properties and results related to distance between probability distributions derived via copula functions. Some inequalities are obtained in view of the dependence and information measures.

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