الفهرس | Only 14 pages are availabe for public view |
Abstract In this thesis, a biased estimator is proposed to combat multi-collinearity in the logistic panel data regression model. This study aims at comparing the performance of biased estimators with Maximum Likelihood was performed using the mean square error criterion by applying to Monte Carlo simulation data and medical data analysis (application study). The proposed estimator is a general estimator which includes other biased estimators, such as the ridge estimator and the Liu estimator as special cases. Furthermore, a Monte Carlo simulation study is given to illustrate some of the theoretical results. Simulation results demonstrate that ridge logistic panel parameter is more efficient than methods. An application is also presented to assess the performance of the proposed ridge estimators. The most significant factors that affect delayed completion of adjuvant chemotherapy in patients with breast cancer. The study results show that the biased estimator is more efficient and better than ML estimators. Moreover, we find that there are very influential factors that affected delayed completion of adjuvant chemotherapy such as Body Surface Area (BSA), Hemoglobin (HGB), Alanine Transaminase (ALT) and Creatinine (SRCR). Finally, both simulation and application results, the proposed estimator is much better than the ML estimator with respect to the MSE criteria. |