using maximum likelihood (ML). The ML method is very sensitive to multicollinearity. Therefore, we present a new Poisson … calculate the mean squared error (MSE) using Monte Carlo simulations. The result from the simulation study shows that the PRR …

the random error and increasing the correlation between the independent variables have negative effect on the MSE. When … the sample size increases the MSE decreases even when the correlation between the independent variables and the variance … of the random error are large. In all situations, the proposed estimators have smaller MSE than the ordinary least …

performance of these new estimators are judged by calculating the mean square error (MSE) using Monte Carlo simulations. In the …

error (MSE). In the simulation study we also calculate the mean value and the standard deviation of k. The average value is … criteria if several estimators of k have the same MSE, then the most stable estimator (with the lowest standard deviation …

. The mean squared of error (MSE) is used as criterion to examine the performance of the proposed estimators when compared … smaller (MSE) than the ordinary least squared estimator (OLS) and Hoerl and Kennard (1970a) estimator (HK). …

(MSE) of the proposed estimators are derived under both null and alternative hypotheses. By studying the MSE criterion, the …

the presence of multicollinearity. After exhibiting the MSE of ridge estimator based on eigenvalues of design matrix, a …, increasing the correlation between the independent variables has positive effect on the MSE (signal-to-noise-ratio). However …> has negative effect on MSE. When the sample size increases the MSE decreases even when the correlation between the …

classical maximum likelihood (ML) method in the presence of multicollinearity since the mean squared error (MSE) of ML becomes … MSE and mean absolute error (MAE) are calculated it is shown that when the Liu estimator is applied with these proposed …