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:: Volume 22, Issue 2 (3-2018) ::
Andishe 2018, 22(2): 93-110 Back to browse issues page
Robust Estimation in Linear Regression with Molticollinearity and Sparse Models
Sara Jazan MS , Seyyed Morteza Amini Dr
MS University of Tehran
Abstract:   (303 Views)

‎One of the factors affecting the statistical analysis of the data is the presence of outliers‎. ‎The methods which are not affected by the outliers are called robust methods‎. ‎Robust regression methods are robust estimation methods of regression model parameters in the presence of outliers‎. ‎Besides outliers‎, ‎the linear dependency of regressor variables‎, ‎which is called multicollinearity‎, ‎the large number of regressor variables with respect to sample size‎, ‎specially in high dimensional sparse models‎, ‎are problems which result in efficiency reduction of inferences in classical regression methods‎. ‎In this paper‎, ‎we first study the disadvantages of classical least squares regression method‎, ‎when facing with outliers‎, ‎multicollinearity and sparse models‎. ‎Then‎, ‎we introduce and study robust and penalized regression methods‎, ‎as a solution to overcome these problems‎. ‎Furthermore‎, ‎considering outliers and multicollinearity or sparse models‎, ‎simultaneously‎, ‎we study penalized-robust regression methods‎. ‎We examine the performance of different estimators introdused in this paper‎, ‎through three different simulation studies‎. ‎A real data set is also analyzed using the proposed methods‎.

Keywords: ‎Outliers‎, ‎Robust regression‎, ‎Multicollinearity‎, ‎Sparse model‎, ‎Penalized regression‎.
Full-Text [PDF 793 kb]   (235 Downloads)    
Type of Study: Applicable | Subject: Special
Received: 2017/03/10 | Accepted: 2018/04/9 | Published: 2018/04/9
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Jazan S, Amini S M. Robust Estimation in Linear Regression with Molticollinearity and Sparse Models. Andishe. 2018; 22 (2) :93-110
URL: http://andisheyeamari.irstat.ir/article-1-478-en.html

Volume 22, Issue 2 (3-2018) Back to browse issues page
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