On Model Selection in Robust LinearRegressionGuoqi

Several model selection criteria which generally can be classiied as the penalized robust method are studied in this paper. Particularly we derive a criterion based on Rissanen's stochastic complexity. Some asymptotic properties concerning strong consistency of selecting the optimal model by these criteria are given under general conditions. Other features like robustness against outliers and eeect of signal-to-noise ratio are also discussed. Finally, examples and simulations are presented to evaluate their nite sample performance. The robust procedure used in this paper considers the gross error in both the response and the independent variables through a generalized Huberization.