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Information Journal Paper

Title

Performance Study of Shrinkage Estimator Under a Linear Constrain in Penalized Regression

Pages

  1-14

Abstract

 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.

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    APA: Copy

    Arast, Mohammmad, ARASHI, MOHAMMAD, & Rabie, Mohammmad reza. (2019). Performance Study of Shrinkage Estimator Under a Linear Constrain in Penalized Regression. JOURNAL OF STATISTICAL SCIENCES, 13(1 ), 1-14. SID. https://sid.ir/paper/124123/en

    Vancouver: Copy

    Arast Mohammmad, ARASHI MOHAMMAD, Rabie Mohammmad reza. Performance Study of Shrinkage Estimator Under a Linear Constrain in Penalized Regression. JOURNAL OF STATISTICAL SCIENCES[Internet]. 2019;13(1 ):1-14. Available from: https://sid.ir/paper/124123/en

    IEEE: Copy

    Mohammmad Arast, MOHAMMAD ARASHI, and Mohammmad reza Rabie, “Performance Study of Shrinkage Estimator Under a Linear Constrain in Penalized Regression,” JOURNAL OF STATISTICAL SCIENCES, vol. 13, no. 1 , pp. 1–14, 2019, [Online]. Available: https://sid.ir/paper/124123/en

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