Robust mixture regression modeling with Pearson type VII distribution - Zhang, Jingyi (Kansas State University, 2013)
A robust estimation procedure for parametric regression models is proposed in the paper by assuming the error terms follow a Pearson type VII distribution. The estimation procedure is implemented by an EM algorithm based on the fact that the Pearson type VII distributions are a scale mixture of a normal distribution and a Gamma distribution. A trimmed version of proposed procedure is also discussed in this paper, which can successfully trim the high leverage points away from the data. Finite sample performance of the proposed algorithm is evaluated by some extensive simulation studies, together with the comparisons made with other existing procedures in the literature.
Keywords: Mixture model; Pearson type VII distribution; EM algorithm; Robust regreesion; Statistics (0463)