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Performance Study of Shrinkage Estimator Under a Linear Constrain in Penalized Regression
Mohammmad Arast , Mohammmad Arashi , Mohammmad reza Rabie
Abstract:   (1208 Views)

Often‎, ‎in high dimensional problems‎, ‎where the number of variables is large the number of observations‎, ‎penalized estimators based on shrinkage methods have better efficiency than the OLS estimator from the prediction error viewpoint‎. In these estimators‎, ‎the tuning or shrinkage parameter plays a deterministic role in variable selection‎. ‎The bridge estimator is an estimator which simplifies to ridge or LASSO estimators varying the tuning parameter‎. ‎In these paper‎, ‎the shrinkage bridge estimator is derived under a linear constraint on regression coefficients and its consistency is proved‎. ‎Furthermore‎, ‎its efficiency is evaluated in a simulation study and a real example‎.

Keywords: Bridge‎, ‎Shrinkage methods.
Full-Text [PDF 475 kb]   (172 Downloads)    
Type of Study: Applied | Subject: Applied Statistics
Received: 2016/11/17 | Accepted: 2018/06/29
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مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences
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