Abstract
In the multiple regression analysis, most frequently occurring problems are the presence of mul-ticollinearity and outliers. They produce undesirable effects on the least squares estimates of regression parameters. The Jackknifed Ridge Regression estimator and M-estimator have been proposed to overcome multicollinearity and outliers respectively. The Jackknifed Ridge Regression estimator is obtained by shrinking the Ordinary Least Squares estimator. Since the Ordinary Least Squares estimator is sensitive to outliers, the Jackknife Ridge Regression estimator is also sensitive to outliers. To overcome the combined problem of multicollinearity and outliers, we propose a new estimator namely, Jackknifed Ridge M-estimator. This estimator is obtained by shrinking an M-estimator instead of the Ordinary Least Squares estimator.
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Jadhav, N.H., Kashid, D.N. A Jackknifed Ridge M-estimator for Regression Model with Multicollinearity and Outliers. J Stat Theory Pract 5, 659–673 (2011). https://doi.org/10.1080/15598608.2011.10483737
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DOI: https://doi.org/10.1080/15598608.2011.10483737