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Robust Estimation in Linear Regression in the presence of Molticollinearity
Sara Jazan MS , Seyyed Morteza Amini Dr
MS University of Tehran
Abstract:   (77 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. ‎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 high dimensional 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 698 kb]   (46 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 in the presence of Molticollinearity. Andishe. 2018; 22 (2)
URL: http://andisheyeamari.irstat.ir/article-1-478-en.html

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