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Variable selection in linear regression in the presence of outliers

Author

Listed:
  • Tejaswi S. Kamble
  • Dattatraya N. Kashid

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.

Suggested Citation

  • Tejaswi S. Kamble & Dattatraya N. Kashid, 2017. "Variable selection in linear regression in the presence of outliers," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 9(2), pages 167-188.
    • Handle: RePEc:ids:injdan:v:9:y:2017:i:2:p:167-188
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