شماره ركورد كنفرانس :
4109
عنوان مقاله :
Asymptotic properties of shrinkage LAD estimators
پديدآورندگان :
Norouzirad M Department of Statistics, School of Mathematical Sciences, Shahrood University of Technology, Shahrood, Iran , Arashi M Department of Statistics, School of Mathematical Sciences, Shahrood University of Technology, Shahrood, Iran , Ahmed S. E Department of Mathematics, Brock University, Canada
كليدواژه :
Asymptotic risk , LAD estimator , LAD , LASSO estimator , Shrinkage method
عنوان كنفرانس :
يازدهمين سمينار ملي احتمال و فرآيندهاي تصادفي
چكيده فارسي :
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.
