A new robust class of skew elliptical distributions

TL;DR

This paper introduces a new robust multivariate skew distribution class that models skewness effectively, is computationally feasible, and outperforms existing distributions in handling high-dimensional data, with practical applications demonstrated.

Contribution

The paper proposes a novel flexible class of multivariate skew distributions with a closed-form density, improving modeling of skewness and computational efficiency.

Findings

Capable of modeling multivariate skewness effectively

Does not suffer heavily from curse of dimensionality

Demonstrated on real-world data sets with practical relevance

Abstract

A new robust class of multivariate skew distributions is introduced. Practical aspects such as parameter estimation method of the proposed class are discussed, we show that the proposed class can be fitted under a reasonable time frame. Our study shows that the class of distributions is capable to model multivariate skewness structure and does not suffer from the curse of dimensionality as heavily as other distributions of similar complexity do, such as the class of canonical skew distributions. We also derive a nested form of the proposed class which appears to be the most flexible class of multivariate skew distributions in literature that has a closed-form density function. Numerical examples on two data sets, i) a data set containing daily river flow data recorded in the UK; and ii) a data set containing biomedical variables of athletes collected by the Australian Institute of Sports (AIS), are demonstrated. These examples further support the practicality of the proposed class on moderate dimensional data sets.