  In this study, we propose a robust mixture regression procedure based on the
skew t distribution to model heavy-tailed and/or skewed errors in a mixture
regression setting. Using the scale mixture representation of the skew t
distribution, we give an Expectation Maximization (EM) algorithm to compute the
maximum likelihood (ML) estimates for the paramaters of interest. The
performance of proposed estimators is demonstrated by a simulation study and a
real data example.
