TY  - JOUR
T1  - An Information-Theoretic Approach for Multivariate Skew Laplace Normal Distributions
AU - Quaez, Uday 
JO  - Journal of Engineering and Applied Sciences
VL  - 17
IS  - 4
SP  - 92
EP  - 102
PY  - 2022
DA  - 2001/08/19
SN  - 1816-949x
DO  - 10.59218\makjeas.2022.92.102
UR  - https://makhillpublications.co/view-article.php?doi=10.59218\makjeas.2022.92.102
KW  - Rényi entropy
KW  - mixture model
KW  - MSLN
KW  - MMSLN and multinomial theorem
AB  - <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>

ER  -