الفهرس 
A Comparative study on some estimators of logistic regression in the presence of multicollinearity
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Abstract
Multicollinearity is one of the major problems in regression estimation methods. The
presence of this problem in logistic regression causes an inflation in the variance of the
Maximum Likelihood (ML) estimator. It can also inaccurate unstable estimates which
affects confidence intervals and hypothesis tests. To overcome this serious problem, some
biased estimators such as: ridge estimator, Liu estimator and Liu-type estimator, were
proposed before as a way of having smaller Mean Squared Error (MSE) than ML estimator. These different biased estimators in the logistic regression are discussed and, the
shrinkage parameters are presented in this thesis. A Monte Carlo simulation is conducted
to assess the performance of ridge and Liu-type estimators, applying some estimators from
the literature, in terms of MSE and Bias criteria. Different levels of correlation between
the explanatory variables, and different sample sizes are considered. It was concluded
that the new applied estimators outperform ML estimator in the ridge and Liu-type estimation. Three empirical applications of real data sets with different sample sizes were
conducted to prove the findings of the simulation study. It is concluded that the results
of the applications agree with the results of the simulation study