@ARTICLE{26543120_38503187_2011, author = {Alexey Shvedov}, keywords = {, Robust regression, Multivariate t-distributionEM algorithm}, title = {Robust Regression Using the t-distribution and the EM Algorithm}, journal = {HSE Economic Journal }, year = {2011}, month = {1}, volume = {15}, number = {1}, pages = {68-87}, url = {https://ej.hse.ru/en/2011-15-1/38503187.html}, publisher = {}, abstract = {The paper deals with a linear regression model. The EM algorithm is popular tool for maximum likelihood estimation of the parameters of regression model. It provides a method of robust regression under the assumption that the disturbances are independent and have identical multivariate t distribution.Previous work focused on the method of maximum likelihood estimation via the EM algorithm under the assumption that the degrees of freedom parameter of the t distribution is a scalar. In this paper, a broader assumption is employed, namely, that the disturbances have a multivariate t distribution with a vector of degrees of freedom. Missing values from the EM algorithm are random matrices.The theoretical results are illustrated in a simulation experiment using several distributions for the error process. Robust procedures are shown to be superior to the method of least squares.}, annote = {The paper deals with a linear regression model. The EM algorithm is popular tool for maximum likelihood estimation of the parameters of regression model. It provides a method of robust regression under the assumption that the disturbances are independent and have identical multivariate t distribution.Previous work focused on the method of maximum likelihood estimation via the EM algorithm under the assumption that the degrees of freedom parameter of the t distribution is a scalar. In this paper, a broader assumption is employed, namely, that the disturbances have a multivariate t distribution with a vector of degrees of freedom. Missing values from the EM algorithm are random matrices.The theoretical results are illustrated in a simulation experiment using several distributions for the error process. Robust procedures are shown to be superior to the method of least squares.} }