Title: Variable selection in linear regression in the presence of outliers

Authors: Tejaswi S. Kamble; Dattatraya N. Kashid

Addresses: Department of Statistics, Shivaji University, Kolhapur (MS), 416004, India ' Department of Statistics, Shivaji University, Kolhapur (MS), 416004, India

Abstract: Majority variable selection methods are based on ordinary least squares (OLS) parameter estimation method. The performance of these variable selection methods is not satisfactory in the presence of outlier observations in the data. Only few variable selection methods based on other parameter estimation methods like M-estimator are proposed by the researchers. In this paper, we propose variable selection method using sum of transformed residual based on the M-estimator in the presence of outlier observation(s). The performance of the proposed method is evaluated through real data and simulated data.

Keywords: variable selection; outlier; M-estimator; sum of transformed residuals.

DOI: 10.1504/IJDATS.2017.085900

International Journal of Data Analysis Techniques and Strategies, 2017 Vol.9 No.2, pp.167 - 188

Received: 29 Jul 2015
Accepted: 26 Jan 2016
Published online: 18 Aug 2017 *

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