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 …

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 …

multicollinearity. This paper introduces a shrinkage estimator for the logit model which is a generalization of the estimator proposed … by Liu (1993) for the linear regression. This new estimation method is suggested since the mean squared error (MSE) of … are highly correlated. Using MSE, the optimal value of the shrinkage parameter is derived and some methods of estimating …

It is known that, when in the linear regression model there is a high degree of multicollinearity, the results obtained … also shown that regression with orthogonal variables makes sense regardless of the existence of serious multicollinearity …

seemingly unrelated regression model when explanatory variables are affected by multicollinearity. To that end, we split the …

non-parametric random forest algorithm. The multicollinearity is detected based on the variance inflation factor. Owing to … the presence of multicollinearity, regularisation techniques such as ridge regression and extensions of the least absolute …