AN ALMOST UNBIASED LIU ESTIMATOR IN LINEAR REGRESSION MODEL WITH MEASUREMENT ERROR

2015 ◽  
Vol 44 (1) ◽  
pp. 77-85
Author(s):  
Jibo Wu ◽  
Abdul Shakoor
Keyword(s):  
Measurement Error ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Liu Estimator ◽  
Almost Unbiased ◽  
Almost Unbiased Liu Estimator

scholarly journals Modified One-Parameter Liu Estimator for the Linear Regression Model

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Adewale F. Lukman ◽  
B. M. Golam Kibria ◽  
Kayode Ayinde ◽  
Segun L. Jegede
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Ridge Regression ◽  
Real Life ◽  
Liu Estimator ◽  
Real Life Data ◽  
Squared Prediction Error ◽  
Simulation Results ◽  
Better Than

Motivated by the ridge regression (Hoerl and Kennard, 1970) and Liu (1993) estimators, this paper proposes a modified Liu estimator to solve the multicollinearity problem for the linear regression model. This modification places this estimator in the class of the ridge and Liu estimators with a single biasing parameter. Theoretical comparisons, real-life application, and simulation results show that it consistently dominates the usual Liu estimator. Under some conditions, it performs better than the ridge regression estimators in the smaller MSE sense. Two real-life data are analyzed to illustrate the findings of the paper and the performances of the estimators assessed by MSE and the mean squared prediction error. The application result agrees with the theoretical and simulation results.

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