TY - JOUR T1 - Studying Limiting Behavior of Shrinkage Estimators in Penalized Regression Model with Rectangular Norm TT - مطالعه رفتار حدی برآوردگرهای انقباضی در مدل رگرسیون تاوانیده با نرم مستطیلی JF - JSS JO - JSS VL - 11 IS - 1 UR - http://jss.irstat.ir/article-1-412-en.html Y1 - 2017 SP - 149 EP - 174 KW - Asymptotic Distribution KW - Improved Estimator KW - LASSO Estimator KW - Prediction Error KW - Rectangular Norm KW - Stein-type Shrinkage Estimator N2 - 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. M3 10.29252/jss.11.1.149 ER -