In this article, we introduce restricted principal components regression (RPCR) estimator by combining the approaches followed in obtaining the restricted least squares estimator and the principal components regression estimator.The performance of the RPCR estimator with respect to the matrix and the generalized mean square error are examined. We also suggest a testing procedure for linear restrictions in principal components regression by using singly and doubly non-central F distribution. © 2009 Taylor & Francis
This article is concerned with the estimation problem of multicollinearity in two seemingly unrelate...
The generalized linear model (Nelder & Wedderburn, 1972) has become an elegant and practical opt...
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The regression coefficient estimates from ordinary least squares (OLS) have a low probability of bei...
In this paper we introduce a class of estimators which includes the ordinary least squares (OLS), th...
Kaçi{dotless}ranlar, and Sakalli{dotless}oglu, [2001. Combining the Liu estimator and the principal ...
In multivariate data analysis, regression techniques predict one set of variables from another while...
In principal component analysis (PCA), the principal components (PC) are linear combinations of the ...
Knottnerus (2003) has proposed the general restriction estimator ( grq ̂ ) for the parameter vector)...
WOS: 000243795800012Kaciranlar, and Sakalhoglu, [2001. Combining the Liu estimator and the principal...
The presence of autocorrelation in errors and multicollinearity among the regressors have undesirabl...
In the presence of multicollinearity, the r - k class estimator is proposed as an alternative to the...
This article is concerned with the estimation problem of multicollinearity in two seemingly unrelate...
The generalized linear model (Nelder & Wedderburn, 1972) has become an elegant and practical opt...
This paper is concerned with the parameter estimator in linear regression model. To overcome the mul...
In this article, we introduce the modified r-k class estimator and the restricted r-k class estimato...
The purpose of this paper is to combine several regression estimators (ordinary least squares (OLS),...
This paper deals with the problem of multicollinearity in a multiple linear regression model with li...
The regression coefficient estimates from ordinary least squares (OLS) have a low probability of bei...
In this paper we introduce a class of estimators which includes the ordinary least squares (OLS), th...
Kaçi{dotless}ranlar, and Sakalli{dotless}oglu, [2001. Combining the Liu estimator and the principal ...
In multivariate data analysis, regression techniques predict one set of variables from another while...
In principal component analysis (PCA), the principal components (PC) are linear combinations of the ...
Knottnerus (2003) has proposed the general restriction estimator ( grq ̂ ) for the parameter vector)...
WOS: 000243795800012Kaciranlar, and Sakalhoglu, [2001. Combining the Liu estimator and the principal...
The presence of autocorrelation in errors and multicollinearity among the regressors have undesirabl...
In the presence of multicollinearity, the r - k class estimator is proposed as an alternative to the...
This article is concerned with the estimation problem of multicollinearity in two seemingly unrelate...
The generalized linear model (Nelder & Wedderburn, 1972) has become an elegant and practical opt...
This paper is concerned with the parameter estimator in linear regression model. To overcome the mul...