Two Stage Robust Ridge Estimators based on robust estimators M, MM, S, LTS are examined in the presence of autocorrelation, multicollinearity and outliers as alternative to Ordinary Least Square Estimator (OLS). The estimator based on S estimator performs better. Mean square error was used as a criterion for examining the performances of these estimators
This paper introduces a new estimator, of ridge parameter k for ridge regression and then evaluated ...
This study aimed to compare the robustness of the OLS method with a robust regression model on data ...
The ridge estimator for handling multicollinearity problem in linear regression model requires the ...
Robust ridge methods based on M, S, MM and GM estimators are examined in the presence of multicollin...
Ridge Regression and Robust Regression Estimators were proposed to deal with the problem of multicol...
In multiple linear regression analysis, multicollinearity and outliers are two main problems. When m...
Common problems in multiple linear regression models are multicollinearity and outliers. In this pap...
Ridge regression, a form of biased linear estimation, is a more appropriate technique than ordinary ...
In the multiple linear regression analysis, the ridge regression estimator is often used to address ...
In the multiple linear regression analysis, the ridge regression estimator is often used to address ...
Abstract. The most popularly used method of estimating the parameters in a linear regression model i...
Hoerl and Kennard (1970) suggested the ridge regression estimator as an alternative to the Ordinary ...
Ridge estimator in linear regression model requires a ridge parameter, K, of which many have been pr...
Ridge estimator in linear regression model requires a ridge parameter, K, of which many have been pr...
One of the main goals of the multiple linear regression model, Y = Xβ + u, is to assess the importan...
This paper introduces a new estimator, of ridge parameter k for ridge regression and then evaluated ...
This study aimed to compare the robustness of the OLS method with a robust regression model on data ...
The ridge estimator for handling multicollinearity problem in linear regression model requires the ...
Robust ridge methods based on M, S, MM and GM estimators are examined in the presence of multicollin...
Ridge Regression and Robust Regression Estimators were proposed to deal with the problem of multicol...
In multiple linear regression analysis, multicollinearity and outliers are two main problems. When m...
Common problems in multiple linear regression models are multicollinearity and outliers. In this pap...
Ridge regression, a form of biased linear estimation, is a more appropriate technique than ordinary ...
In the multiple linear regression analysis, the ridge regression estimator is often used to address ...
In the multiple linear regression analysis, the ridge regression estimator is often used to address ...
Abstract. The most popularly used method of estimating the parameters in a linear regression model i...
Hoerl and Kennard (1970) suggested the ridge regression estimator as an alternative to the Ordinary ...
Ridge estimator in linear regression model requires a ridge parameter, K, of which many have been pr...
Ridge estimator in linear regression model requires a ridge parameter, K, of which many have been pr...
One of the main goals of the multiple linear regression model, Y = Xβ + u, is to assess the importan...
This paper introduces a new estimator, of ridge parameter k for ridge regression and then evaluated ...
This study aimed to compare the robustness of the OLS method with a robust regression model on data ...
The ridge estimator for handling multicollinearity problem in linear regression model requires the ...