Shifted Liu-Type Estimator in The Linear Regression
DOI:
https://doi.org/10.20956/j.v19i1.21136Keywords:
Multicollinearity, Biased estimation, Ridge estimator, Liu estimatorAbstract
The methods to solve the problem of multicollinearity have an important issue in the linear regression. The Liu-type estimator is one of these methods used to reduce its effect. This estimator is an estimator with two parameters denoted and . Kurnaz and Akay (2015) [6] introduced a new approach for the Liu-type estimator and called it new Liu-type (NL) estimator. This proposed estimator is based on a continuous function of rather than two parameters and includes OLS, ridge estimator, Liu estimator, and some estimators with two biasing parameters as special cases. This study aimed to improve the NL estimator by shifting. The performance of the shifted NL estimator is compared to the NL estimator and other estimators depending on the mean squared error (MSE) criterion. The real data example and simulation study reveal that the SNL estimator can be a good selection in the linear regression model.
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