Uday J. Quaez, 
    An Information-Theoretic Approach for Multivariate Skew Laplace Normal Distributions,
    Journal of Engineering and Applied Sciences,
    Volume 17,Issue 4,
    2022,
    Pages 92-102,
    ISSN 1816-949x,
    10.59218\makjeas.2022.92.102,
    (https://makhillpublications.co/view-article.php?doi=10.59218\makjeas.2022.92.102)
    Abstract: <p style="text-align:justify">Due to its flexibility, the skewness distributions (univariate and multivariate) have received widespread attention over the last two decades because their become widely used in the modelling and analysis of skewed datasets. The main goal of this paper is to introduce asymptotic expressions for entropy of multivariate skew Laplace normal distribution to deal with the issue by providing a flexible model for modeling skewness and heavy tiredness simultaneously. Thus, we extend this study to the class of mixture model of these distributions. In addition, upper and lower bounds of entropy is determined for proposed models. Finally, we give a real data examples to illustrate the behavior of information. A simulation study and a real data example are also provided to illustrate the information behavior of MSLN and MMSLN distributions for modeling data sets in multivariate settings.</p>

    Keywords: Rényi entropy; mixture model; MSLN; MMSLN and multinomial theorem