Using mixtures in seemingly unrelated linear regression models with   non-normal errors
  Seemingly unrelated linear regression models are introduced in which the distribution of the errors is a finite mixture of Gaussian components. Identifiability conditions are provided. The score vector and the Hessian matrix are derived. Parameter estimation is performed using the maximum likelihood method and an Expectation-Maximisation algorithm is developed. The usefulness of the proposed methods and a numerical evaluation of their properties are illustrated through the analysis of a real dataset. 
Giuliano Galimberti, Elena Scardovi, Gabriele Soffritti
stat.ME