Abstract. This paper introduces a new biased estimator, namely, almost unbiased Liu estimator (AULE) of β for the multiple linear regression model with heteroscedastics and/or correlated errors and suffers from the problem of multicollinearity. The properties of the proposed estimator is discussed and the performance over the generalized least squares (GLS) estimator, ordinary ridge regression (ORR) estimator (Trenkler [20]), and Liu estimator (LE) (Kaçiranlar [10]) in terms of matrix mean square error criterion are investigated. The optimal values of d for Liu and almost unbiased Liu estimators have been obtained. Finally, a simulation study has been conducted which indicated that under certain conditions on d, the proposed estimator perf...
Multiple linear regression models are frequently used in predicting (forecasting) unknown values of ...
In this paper, we introduce the new biased estimator to deal with the problem of multicollinearity. ...
We propose the Liu estimator and the Liu predictor via the penalized log-likelihood approach in line...
This paper introduces a new biased estimator, namely, almost unbiased Liu estimator (AULE) of β...
Motivated by the ridge regression (Hoerl and Kennard, 1970) and Liu (1993) estimators, this paper pr...
WOS: A1995RG21200008In this paper, we derive the almost unbiased generalized Liu estimator and exami...
Consider the regression model y = ß01 + Xß + ?. Recently, the Liu estimator, which is an alternative...
SUMMARY. This paper deals with the standard multiple linear regression model (y,Xβ, σ2I), where the ...
This article is concerned with the parameter estimation in partly linear regression models when the ...
Multicollinearity among the explanatory variables seriously effects the maximum likelihood estimator...
Multicollinearity problem in logistic regression causes an inflation in the variance of the Maximum ...
In the presence of collinearity certain biased estimation procedures like ridge regression, generali...
Multiple linear interferences are a fundamental obstacle in many standard models. This problem appea...
The performances of two biased estimators for the general linear regression model under conditions o...
The problem of multicollinearity and outliers in the dataset can strongly distort ordinary least-squ...
Multiple linear regression models are frequently used in predicting (forecasting) unknown values of ...
In this paper, we introduce the new biased estimator to deal with the problem of multicollinearity. ...
We propose the Liu estimator and the Liu predictor via the penalized log-likelihood approach in line...
This paper introduces a new biased estimator, namely, almost unbiased Liu estimator (AULE) of β...
Motivated by the ridge regression (Hoerl and Kennard, 1970) and Liu (1993) estimators, this paper pr...
WOS: A1995RG21200008In this paper, we derive the almost unbiased generalized Liu estimator and exami...
Consider the regression model y = ß01 + Xß + ?. Recently, the Liu estimator, which is an alternative...
SUMMARY. This paper deals with the standard multiple linear regression model (y,Xβ, σ2I), where the ...
This article is concerned with the parameter estimation in partly linear regression models when the ...
Multicollinearity among the explanatory variables seriously effects the maximum likelihood estimator...
Multicollinearity problem in logistic regression causes an inflation in the variance of the Maximum ...
In the presence of collinearity certain biased estimation procedures like ridge regression, generali...
Multiple linear interferences are a fundamental obstacle in many standard models. This problem appea...
The performances of two biased estimators for the general linear regression model under conditions o...
The problem of multicollinearity and outliers in the dataset can strongly distort ordinary least-squ...
Multiple linear regression models are frequently used in predicting (forecasting) unknown values of ...
In this paper, we introduce the new biased estimator to deal with the problem of multicollinearity. ...
We propose the Liu estimator and the Liu predictor via the penalized log-likelihood approach in line...