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Studying Limiting Behaviour of Shrinkage Estimators in Penalized Regression Model with Rectangular Norm
Mina Norouzirad , Mohammad Arashi
Abstract:   (1582 Views)

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.

Keywords: Asymptotic distribution, Improved estimator, LASSO estimator, Prediction error, Rectangular norm, Stein-type shrinkage estimator
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Type of Study: Research | Subject: Theoritical Statistics
Received: 2015/10/27 | Accepted: 2017/02/9 | Published: 2017/02/13
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Norouzirad M, Arashi M. Studying Limiting Behaviour of Shrinkage Estimators in Penalized Regression Model with Rectangular Norm. J. of Stat. Sci.. 2017; 11 (1)
URL: http://jss.irstat.ir/article-1-412-en.html
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مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences
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