Research Article
Year 2014,
Volume: 43 Issue: 5, 827 - 841, 01.10.2014
Abstract
In multiple regression analysis, the independent variables should be
uncorrelated within each other. If they are highly intercorrelated, this
serious problem is called multicollinearity. There are several methods
to get rid of this problem and one of the most famous one is the ridge
regression. In this paper, we will propose some modified ridge parameters. We will compare our estimators with some estimators proposed
earlier according to mean squared error (MSE) criterion. All results
are calculated by a Monte Carlo simulation. According to simulation
study, our estimators perform better than the others in most of the
situations in the sense of MSE.
uncorrelated within each other. If they are highly intercorrelated, this
serious problem is called multicollinearity. There are several methods
to get rid of this problem and one of the most famous one is the ridge
regression. In this paper, we will propose some modified ridge parameters. We will compare our estimators with some estimators proposed
earlier according to mean squared error (MSE) criterion. All results
are calculated by a Monte Carlo simulation. According to simulation
study, our estimators perform better than the others in most of the
situations in the sense of MSE.
Keywords
Multicollinearity multiple linear regression ridge regression ridge estimator Monte Carlo Simulation
References
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There are 1 citations in total.
Details
| Primary Language | English |
|---|---|
| Journal Section | Statistics |
| Authors | |
| Publication Date | October 1, 2014 |
| Published in Issue | Year 2014 Volume: 43 Issue: 5 |