English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Shannon and Renyi entropies are quantitative measures of uncertainty in adata set. They are developed by Renyi in the context of entropy theory. Thesemeasures have been studied in the case of the multivariate t-distributions. Weextend these tools to the class of multivariate skew t-distributions and thento more families of finite mixture of multivariate skew t distributions. Inparticular, by using generalized Holder inequality and some properties ofmultinomial theorem, we find upper and lower bounds of entropies for thesefamilies. An approximate value of these entropies can be calculated. Inaddition, an asymptotic expression for Renyi entropy is given by approximationand by using some inequalities and properties of Lp spaces. Finally, we give areal data examples to illustrate the behavior of entropy of the mixture modelunder consideration.

Renyi and Shannon Entropies of Finite Mixtures of Multivariate Skew t-distributions