مقارنة بين طرائق تقدير انحدار الحرف العامة في معالجة مشكلة التعدد الخطي شبه التام مع تطبيق عملي == A Comparisons Among The Generalized Ridge Regression Methods of Estimators Under Multicollinearity Problem With A Praxis
Author name:
حنين مراد يوسف الصالحي
Supervisor name:
سجى محمد حسين الهاشمي
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:
07T3584 - p.pdf
Abstract:
يلقى موضوع خرق احد فروض الانحدار اهتماما واسعا وواضحا في معظم الدراسات لذلك تنبع اهمية هذا البحث من خلال الكشف عن احد هذه الفروض ومعالجتها والمتمثلة بـ وجود علاقة خطية بين اثنين او اكثر من المتغيرات التوضيحية والتي تدعى بمشكلة التعدد الخطي (Multicollinea | The violation of regression assumptions is one of the interesting topics in many fields. This thesis deals with multicollinearity problem by using some of generalized ridge regression methods (GRR, MJR, GJR, GL, AUGRR, and AUGL) as well as our proposed method (Almost unbiased Generalized Jackknife Ridge) (AUGJR). In addition, these methods are compared with parameter (k) in ridge and parameter (Liu D). These methods are evaluated based on the mean squared error (MSE) to obtain the best method of these methods with the suitable parameter.To evaluate these methods, we use simulation studies by using the R statistical program. Five variables are simulated with different sample sizes (n=15,20,60,300), different variances (0.16,0.13,0.18) and different correlation coefficients (r=0.16,0.13 and 0.18) with (s=10 and 2). The results show that AUGL, AUGJR and AUGRR are the best methods in terms of the MSE. The differences are very small among them. The methods are also evaluated based on real data example (Rail transport for the passengers and cargo), which is obtained from Iraqi Ministry of Planning.We first detected the problem of multicollinearity by using the Variance Inflation Factor (VIF) and Condition Index (CI). Then, we build a model of revenue for transportation after they have been estimating its features at the best methods.
