Study of Some Kinds of Ridge Regression Estimators in Linear Regression Model
DOI:
https://doi.org/10.25130/tjps.v25i5.301Abstract
In linear regression model, the biased estimation is one of the most commonly used methods to reduce the effect of the multicollinearity. In this paper, a simulation study is performed to compare the relative efficiency of some kinds of biased estimators as well as for twelve proposed estimated ridge parameter (k) which are given in the literature. We propose some new adjustments to estimate the ridge parameter. Finally, we consider a real data set in economics to illustrate the results based on the estimated mean squared error (MSE) criterion.
According to the results, all the proposed estimators of (k) are superior to ordinary least squared estimator (OLS), and the superiority among them based on minimum MSE matrix will change according to the sample under consideration.
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