Abstract:
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.
