Abstract
In the context of linear regression models, the least absolute deviations (LAD) is used when there are outliers. Selecting significant variables is very important; however, by choosing these variables, some information may be sacrificed. To prevent this, in our proposal, we can combine the full model estimates toward the candidate sub model, resulting in improved estimators in risk sense. Advantages of the proposed estimators over the usual LAD estimator are demonstrated through a real data example.
Type
Publication
11th Seminar on Probability and Stochastic Processes
