In this paper, we introduce a generalization—referred to as the beta Pareto distribution, generated from the logit of a beta random variable. We provide a comprehensive treatment of the mathematical properties of the beta Pareto distribution. We derive expressions for the kth moments of the distribution, variance, skewness, kurtosis, mean deviation about the mean, mean deviation about the median, Rényi entropy, Shannon entropy. We also discuss simulation issues, estimation of parameters by the methods of moments and maximum likelihood.
Excess kurtosis of a univariate random variable is defined as its kurtosis minus 3, i.e. the kurtosis of a normal distribution. Excess kurtosis is a one of a dispersion measures. This parameter provides the information about peakedness and tail weight of a distribution compared to normal distribution. In the paper we propose a generalization of this characteristic for random vectors and analyze its basic properties. Moreover, we introduce the form of excess kurtosis for the selected multivariate distribution.
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