Robust mixture regression model fitting by Laplace distribution - Xing, Yanru (Kansas State University, 2013)
A robust estimation procedure for mixture linear regression models is proposed in this report by assuming the error terms follow a Laplace distribution. EM algorithm is imple- mented to conduct the estimation procedure of missing information based on the fact that the Laplace distribution is a scale mixture of normal and a latent distribution. Finite sample performance of the proposed algorithm is evaluated by some extensive simulation studies, together with the comparisons made with other existing procedures in this literature. A sensitivity study is also conducted based on a real data example to illustrate the application of the proposed method.
Keywords: EM algorithm; Laplace distribution; Least absolute deviation; Mixture regression model; Statistics (0463)