Singh et al. (1986) proposed an almost unbiased ridge estimator using Jackknife method that required transformation of the regression parameters. This article shows that the same method can be used to derive the Jackknifed ridge estimator of the original (untransformed) parameter without transformation. This method also leads in deriving easily the second order Jackknifed ridge that may reduce the bias further. We further investigate the performance of these estimators along with a recent method by Batah et al. (2008) called modified Jackknifed ridge theoretically as well as numerically
Ridge estimator in linear regression model requires a ridge parameter, K, of which many have been pr...
AbstractHoerl and Kennard (1970a) introduced the ridge regression estimator as an alternative to the...
Ridge regression, a form of biased linear estimation, is a more appropriate technique than ordinary ...
Abstract. A common problem in multiple regression models is multicollinearity, which pro-duces undes...
Several remediation measures have been developed to circumvent the problem of collinearity in Genera...
Trenkler [Trenkler, G., 1984. On the performance of biased estimators in the linear regression model...
The ridge regression model has been consistently demonstrated to be an attractive shrinkage method t...
In 2003, Liu proposed a new estimator dealing with the problem of multicollinearity in linear regre...
Ordinary and jack-knifed ridge type estimators are compared for different measures of goodness. Alth...
The estimation of ridge parameter is an important problem in the ridge regression method, which is w...
<p><em>Ordinary least square is a parameter estimations for minimizing residual sum of squares. If t...
In this study, we proposed an alternative biased estimator. The linear regression model might lead t...
The purpose of this article is to obtain the jackknifed ridge predictors in the linear mixed models ...
This paper introduces a new estimator, of ridge parameter k for ridge regression and then evaluated ...
Regression models explore the relationship between the response variable and one or more explanatory...
Ridge estimator in linear regression model requires a ridge parameter, K, of which many have been pr...
AbstractHoerl and Kennard (1970a) introduced the ridge regression estimator as an alternative to the...
Ridge regression, a form of biased linear estimation, is a more appropriate technique than ordinary ...
Abstract. A common problem in multiple regression models is multicollinearity, which pro-duces undes...
Several remediation measures have been developed to circumvent the problem of collinearity in Genera...
Trenkler [Trenkler, G., 1984. On the performance of biased estimators in the linear regression model...
The ridge regression model has been consistently demonstrated to be an attractive shrinkage method t...
In 2003, Liu proposed a new estimator dealing with the problem of multicollinearity in linear regre...
Ordinary and jack-knifed ridge type estimators are compared for different measures of goodness. Alth...
The estimation of ridge parameter is an important problem in the ridge regression method, which is w...
<p><em>Ordinary least square is a parameter estimations for minimizing residual sum of squares. If t...
In this study, we proposed an alternative biased estimator. The linear regression model might lead t...
The purpose of this article is to obtain the jackknifed ridge predictors in the linear mixed models ...
This paper introduces a new estimator, of ridge parameter k for ridge regression and then evaluated ...
Regression models explore the relationship between the response variable and one or more explanatory...
Ridge estimator in linear regression model requires a ridge parameter, K, of which many have been pr...
AbstractHoerl and Kennard (1970a) introduced the ridge regression estimator as an alternative to the...
Ridge regression, a form of biased linear estimation, is a more appropriate technique than ordinary ...