Abstract Skew-t-normal distribution is one of the most important statistical tools to analyze the obvious peak and fat tail data. A linear mixture joint location and scale model with skew-t-normal data is proposed in this paper. The maximum likelihood estimation of the unknown parameters of this model is investigated based on Expectation Maximization (EM) algorithm and Newton-Raphson method. Furthermore, the proposed procedure works satisfactorily through Monte Carlo experiments. Finally, a real example shows that both this model and method are useful and effective.
ZHU Zhi-e, WU Liu-cang, DAI Lin. Parameter estimation for linear joint location and scale models with mixture skew-t-normal data. Applied Mathematics A Journal of Chinese Universities, 2016, 31(4): 379-389.
