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.
Citation:
Shvedov A. (2011) Robastnaya regressiya s primeneniem t-raspredeleniya i EM-algoritma [Robust Regression Using the t-distribution and the EM Algorithm]. HSE Economic Journal , vol. 15, no 1, pp. 68-87 (in Russian)
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