Add to ORKGThe 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
Ridge regression is used to circumvent the problem of multicollinearity among predictors and many es...
The ridge estimator for handling multicollinearity problem in linear regression model requires the ...
The Linear regression model is one of the most widely used models in differentfields of study. The m...
The performances of two biased estimators for the general linear regression model under conditions o...
Least square estimators in multiple linear regressions under multicollinearity become unstable as th...
During the past years, different kinds of estimators have been proposed as alternatives to the Ordin...
ABSTRACTPresence of collinearity among the explanatory variables results in larger standard errors o...
Ridge regression, a form of biased linear estimation, is a more appropriate technique than ordinary ...
AbstractRidge regression estimator has been introduced as an alternative to the ordinary least squar...
In this paper, a number of procedures have been proposed for developing new biased estimators of see...
The general linear regression model has been one of the most frequently used models over the years, ...
WOS:000347016500016In multiple regression analysis, the independent variables should beuncorrelated ...
One of the main goals of the multiple linear regression model, Y = Xβ + u, is to assess the importan...
The inefficiency of the ordinary least square estimator for the parameter estimation of a linear reg...
The ridge regression-type (Hoerl and Kennard, 1970) and Liu-type (Liu, 1993) estimators are consiste...
Ridge regression is used to circumvent the problem of multicollinearity among predictors and many es...
The ridge estimator for handling multicollinearity problem in linear regression model requires the ...
The Linear regression model is one of the most widely used models in differentfields of study. The m...
The performances of two biased estimators for the general linear regression model under conditions o...
Least square estimators in multiple linear regressions under multicollinearity become unstable as th...
During the past years, different kinds of estimators have been proposed as alternatives to the Ordin...
ABSTRACTPresence of collinearity among the explanatory variables results in larger standard errors o...
Ridge regression, a form of biased linear estimation, is a more appropriate technique than ordinary ...
AbstractRidge regression estimator has been introduced as an alternative to the ordinary least squar...
In this paper, a number of procedures have been proposed for developing new biased estimators of see...
The general linear regression model has been one of the most frequently used models over the years, ...
WOS:000347016500016In multiple regression analysis, the independent variables should beuncorrelated ...
One of the main goals of the multiple linear regression model, Y = Xβ + u, is to assess the importan...
The inefficiency of the ordinary least square estimator for the parameter estimation of a linear reg...
The ridge regression-type (Hoerl and Kennard, 1970) and Liu-type (Liu, 1993) estimators are consiste...
Ridge regression is used to circumvent the problem of multicollinearity among predictors and many es...
The ridge estimator for handling multicollinearity problem in linear regression model requires the ...
The Linear regression model is one of the most widely used models in differentfields of study. The m...