An Information-Theoretic Approach for Multivariate Skew-t Distributions and Applications
نویسندگان
چکیده
Shannon and Rényi entropies are two important measures of uncertainty for data analysis. These have been studied multivariate Student-t skew-normal distributions. In this paper, we extend the entropy to skew-t finite mixture (FMST) This class flexible distributions allows handling asymmetry tail weight behavior simultaneously. We find upper lower bounds these families. Numerical simulations illustrate results several scenarios: symmetry/asymmetry light/heavy-tails. Finally, present applications our findings a swordfish length-weight dataset FMST distribution. Comparisons with counterparts—the normal distributions—are also presented.
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ژورنال
عنوان ژورنال: Mathematics
سال: 2021
ISSN: ['2227-7390']
DOI: https://doi.org/10.3390/math9020146