RT - Journal Article T1 - Studying Limiting Behavior of Shrinkage Estimators in Penalized Regression Model with Rectangular Norm JF - JSS YR - 2017 JO - JSS VO - 11 IS - 1 UR - http://jss.irstat.ir/article-1-412-en.html SP - 149 EP - 174 K1 - Asymptotic Distribution K1 - Improved Estimator K1 - LASSO Estimator K1 - Prediction Error K1 - Rectangular Norm K1 - Stein-type Shrinkage Estimator AB - Penalized estimators for estimating regression parameters have been considered by many authors for many decades. Penalized regression with rectangular norm is one of the mainly used since it does variable selection and estimating parameters, simultaneously. In this paper, we propose some new estimators by employing uncertain prior information on parameters. Superiority of the proposed shrinkage estimators over the least absoluate and shrinkage operator (LASSO) estimator is demonstrated via a Monte Carlo study. The prediction rate of the proposed estimators compared to the LASSO estimator is also studied in the US State Facts and Figures dataset. LA eng UL http://jss.irstat.ir/article-1-412-en.html M3 10.29252/jss.11.1.149 ER -