A Review on the Biasing Parameters of Ridge Regression Estimator in LRM


  • Madhulika Dube Professor & Head Department of Statistics, M. D. University, Rohtak
  • Isha Research Scholar Department of Statistics M. D. University, Rohtak




Ordinary Least Squares Estimator, Multicollinearity, Ridge Regression, Biasing Parameter, Ridge Parameters


Ridge regression is one of the most widely used biased estimators in the presence of multicollinearity, preferred over unbiased ones since they have a larger probability of being closer to the true parametric value. Being the modification of the least squares method it introduces a biasing parameter to reduce the length of the parameter under study. As these biasing parameters depend upon the unknown quantities, extensive work has been carried out by several authors to work out the best one. Owing to the fact that over the years large numbers of biasing parameters have been proposed and studied, this article presents an annotated bibliography along with the review on various biasing parameter available.


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How to Cite

Madhulika Dube, and Isha. 2014. “A Review on the Biasing Parameters of Ridge Regression Estimator in LRM”. Mathematical Journal of Interdisciplinary Sciences 3 (1):73-82. https://doi.org/10.15415/mjis.2014.31007.