Multivariate Skew t-Distribution: Asymptotics for Parameter Estimators and Extension to Skew t-Copula

Symmetric elliptical distributions have been intensively used in data modeling and robustness studies. The area of applications was considerably widened after transforming elliptical distributions into the skew elliptical ones that preserve several good properties of the corresponding symmetric distributions and increase possibilities of data modeling. We consider three-parameter p-variate skew t-distribution where p-vector mu is the location parameter, Sigma : p x p is the positive definite scale parameter, p-vector alpha is the skewness or shape parameter, and the number of degrees of freedom nu is fixed. Special attention is paid to the two-parameter distribution when mu = 0 that is useful for construction of the skew t-copula. Expressions of the parameters are presented through the moments and parameter estimates are found by the method of moments. Asymptotic normality is established for the estimators of Sigma and alpha. Convergence to the asymptotic distributions is examined in simulation experiments.

Automated summary

Symmetric elliptical distributions have been widely used in data modeling and robustness studies. After transforming into skew elliptical distributions, the area of applications has been significantly expanded. The study focuses on the skew t-distribution, with special attention given to the case when mu = 0 for constructing skew t-copula.