The Liu estimator has consistently been demonstrated to be an attractive shrinkage method for reducing the effects of multicollinearity. The Poisson regression model is a well-known model in applications when the response variable consists of count data. However, it is known that multicollinearity negatively affects the variance of the maximum likelihood estimator (MLE) of the Poisson regressio...

In the linear regression models with AR (1) error structure when collinearity exists, stochastic linear restrictions or modifications of biased estimators (including Liu estimators) can be used to reduce the estimated variance of the regression coefficients estimates. In this paper, the combination of the biased Liu estimator and stochastic linear restrictions estimator is considered to overcom...

The Liu-type estimator is one of the shrink estimators that used to remedy for a problem multicollinearityin SUR model, but it sensitive outlier. In this paper, we introduce S (SLiu-type) and MM (MMLiu-type) model. These merge with S-estimator MM-estimator which makes have high robustness at different level efficiency same time prevents bad effects multicollinearity. Moreover, get more robust f...

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) esti...

In this study a new two-parameter estimator which includes the ordinary least squares (OLS), the principal components regression (PCR) and the Liu-type estimator is proposed. Conditions for the superiority of this new estimator over the PCR, r-k class estimator and Liu-type estimator are derived. Furthermore the performance of this estimator is compared with the other estimators in different co...