Rényi Entropy for Mixture Model of Multivariate Skew Laplace distributions

Author:

Abid Salah H.,Quaez Uday J.

Abstract

Abstract Rényi entropy is an important concept developed by Rényi in information theory. In this paper, we study in detail this measure of information in cases multivariate skew Laplace distributions then we extend this study to the class of mixture model of multivariate skew Laplace distributions. The upper and lower bounds of Rényi entropy of mixture model are determined. In addition, an asymptotic expression for Rényi entropy is given by the approximation. Finally, we give a real data example to illustrate the behavior of entropy of the mixture model under consideration.

Publisher

IOP Publishing

Subject

General Physics and Astronomy

Reference16 articles.

1. Finite Mixtures of Multivariate Skew t-distributions;Lee;Statistics and Computing,2014

2. Bounds on Rényi and Shannon Entropies for Finite Mixtures of Multivariate Skew-Normal Distributions: Applications to Swordfish (Xiphias gladius Linnaeus);Contreras-Reyes;Entropy,2016

3. Shannon Entropy and Mutual information for Multivariate Skew Elliptical Distributions;Arellano-Valle,2012

4. The Multivariate Skew-normal distribution;Azzalini;Biometrika,1996

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