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Full-Text Articles in Applied Statistics
A Proposed Ridge Parameter To Improve The Least Square Estimator, Ghadban Khalaf
A Proposed Ridge Parameter To Improve The Least Square Estimator, Ghadban Khalaf
Journal of Modern Applied Statistical Methods
Ridge regression, a form of biased linear estimation, is a more appropriate technique than ordinary least squares (OLS) estimation in the case of highly intercorrelated explanatory variables in the linear regression model Y = β + u. Two proposed ridge regression parameters from the mean square error (MSE) perspective are evaluated. A simulation study was conducted to demonstrate the performance of the proposed estimators compared to the OLS, HK and HKB estimators. Results show that the suggested estimators outperform the OLS and the other estimators regarding the ridge parameters in all situations examined.
Robust Regression Estimates In The Prediction Of Latent Variables In Structural Equation Models, Marcelo Angelo Cirillo, Lúcia Pereira Barroso
Robust Regression Estimates In The Prediction Of Latent Variables In Structural Equation Models, Marcelo Angelo Cirillo, Lúcia Pereira Barroso
Journal of Modern Applied Statistical Methods
The incorporation of the robust regression methods Least Median Square (LMS) and Least Trimmed Squares (LTS) is proposed in structural equation modeling. Results show that, in situations of high deviations of symmetry, the evaluated methods would be recommended for applications including smaller sample sizes.
Improved Estimator In The Presence Of Multicollinearity, Ghadban Khalaf
Improved Estimator In The Presence Of Multicollinearity, Ghadban Khalaf
Journal of Modern Applied Statistical Methods
The performances of two biased estimators for the general linear regression model under conditions of collinearity are examined and a new proposed ridge parameter is introduced. Using Mean Square Error (MSE) and Monte Carlo simulation, the resulting estimator’s performance is evaluated and compared with the Ordinary Least Square (OLS) estimator and the Hoerl and Kennard (1970a) estimator. Results of the simulation study indicate that, with respect to MSE criteria, in all cases investigated the proposed estimator outperforms both the OLS and the Hoerl and Kennard estimators.