The Weight Function in the Bounded Influence Regression Quantile Estimator for the AR(1) Model with Additive Outliers Jung Byoung Cheol; Han Sang Moon;
In this study, we investigate the effects of the weight function in the bounded influence regression quantile (BIRQ) estimator for the AR(l) model with additive outliers. In order to down-weight the outliers of X -axis, the Mallows` (1973) weight function has been commonly used in the BIRQ estimator. However, in our Monte Carlo study, the BIRQ estimator using the Tukey`s bisquare weight function shows less MSE and bias than that of using the Mallows` weight function or Huber`s weight function. Thus, the use of the Tukey`s weight function is recommended in the BIRQ estimator for our model.