This paper introduces a new estimator, of ridge parameter k for ridge regression and then evaluated by Monte Carlo simulation. We examine the performance of the proposed estimators compared with other well-known estimators for the model with heteroscedastics and/or correlated errors, outlier observations, non-normal errors and suffer from the problem of multicollinearity. It is shown that proposed estimators have a smaller MSE than the ordinary least squared estimator (LS), Hoerl and Kennard (1970) estimator (RR), jackknifed modified ridge (JMR) estimator, and Jackknifed Ridge M‑estimator (JRM).
Dorugade, Ashok Vithoba
"Improved Ridge Estimator in Linear Regression with Multicollinearity, Heteroscedastic Errors and Outliers,"
Journal of Modern Applied Statistical Methods: Vol. 15
, Article 23.
Available at: http://digitalcommons.wayne.edu/jmasm/vol15/iss2/23