Rényi entropy and complexity measure for skew-gaussian distributions and related families

Abstract

In this paper, we provide the Rényi entropy and complexity measure for a novel, flexible class of skew-gaussian distributions and their related families, as a characteristic form of the skew-gaussian Shannon entropy. We give closed expressions considering a more general class of closed skew-gaussian distributions and the weighted moments estimation method. In addition, closed expressions of Rényi entropy are presented for extended skew-gaussian and truncated skew-gaussian distributions. Finally, additional inequalities for skew-gaussian and extended skew-gaussian Rényi and Shannon entropies are reported.

13 pages

References in corpus (2)

• Statistical applications of the multivariate skew-normal distribution
• An intermediate distribution between Gaussian and Cauchy distributions

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