استعمال طريقتي انحدار الحرف والمركبات الرئيسية في تقدير معلمات انموذج اللوجستك في حالة وجود مشكلة التعدد الخطي مع تطبيق عملي == Use The Methods of Ridge Regression And Principle Components To Estimate The Parameters of Logistic Model Under Multicollinearity With Practical Application
Author name:
علي حسين فلوح
Supervisor name:
سهيل نجم عبود
General topic:
Administration and Economics
Specific topic:
Statistics
Degree:
Master
University:
University of Baghdad - Faculty Of Administration And Economics - Department Of Statistics
Language:
Arabic
University location:
Baghdad
First pages:
07T3873 - p.pdf
Abstract:
ان اكثر الدراسات في انموذج انحدار اللوجستيك تاخذ طابعا اكثر تقدما في عملية التحليل الاحصائي الدقيق الذي يمكن من خلاله الحصول على مقدرات عالية الكفاءة.ان مشكلة التعدد الخطي يمكن ان تظهر في انموذج درجة الميل (Propensity Score Model) عند تقدير متوسط تاثير | More studies on the topic of logistics regression model takes a more advanced in the process of careful statistical analysis which aims to get estimators with a high level of efficiency.The problem of Multicollinearity can appear in the model of propensity scores (PS) when estimating the average treatment effects (ATEs). In this thesis, using the methods of logistic ridge regression, logistic principle components regression an alternative to the maximum likelihood estimates in the propensity scores model. The average treatment effects (ATEs) estimators adopted the method of inverse probability weighted (IPW). logistic ridge regression (LRR), principle components logistical regression (PCLR), and maximum likelihood (ML) used to estimate the propensity scores (PS), The logistics regression model and the variable (W_i) and the Bernoulli distribution in depended variables then probability (?_i) and then estimate the average treatment effects (ATEs), where the use of simulations (Monte Carlo) to generate tracking data model of logistic regression and Multicollinearity problem depending on various factors, from simple correlation coefficient values and sample size and the number of independent variables and the constant value plus the adoption of different designs of propensity score in simulation study, this is due to the fact that estimates the average treatment effects (ATEs) her strengths and different accounts. And we use Bias and the mean squares error (MSE) as criteria for comparing methods of estimation.The results that have been obtained using a simulation study indicates that the Bias and the mean square error (MSE) depend on the sample size and the degree of the correlation as well as the design propensity score model. I have observed that the estimation method of logistic ridge regression (LRR) and principle component logistic regression (PCLR) was the best of the maximum likelihood estimates (MLE).In addition to the simulation study, has been applied method of logistic ridge regression (depending on(2 - 16)) on the data of the fact disease, kidney failure, which was obtained from the (Al_Emamyn city hospital) for different models to estimate the propensity scores and then estimate average treatment effect (ATE).
