Renyi and Shannon Entropies of Finite Mixtures of Multivariate Skew t-distributions (1901.10569v1)

Abstract: Shannon and Renyi entropies are quantitative measures of uncertainty in a data set. They are developed by Renyi in the context of entropy theory. These measures have been studied in the case of the multivariate t-distributions. We extend these tools to the class of multivariate skew t-distributions and then to more families of finite mixture of multivariate skew t distributions. In particular, by using generalized Holder inequality and some properties of multinomial theorem, we find upper and lower bounds of entropies for these families. An approximate value of these entropies can be calculated. In addition, an asymptotic expression for Renyi entropy is given by approximation and by using some inequalities and properties of Lp spaces. Finally, we give a real data examples to illustrate the behavior of entropy of the mixture model under consideration.