استعمال اشجار الانحدار التصنيفية والانحدار اللوجستي في تقدير انموذج تجميعي والمقارنة بينهما مع تطبيق عملي == Using Classification Regression Trees And Logistic Regression To Estimate Additive Model Comparison With Application
Author name:
سهاد احمد احمد
Supervisor name:
عمر عبد المحسن علي القيسي
General topic:
Administration and Economics
Specific topic:
Statistics
Degree:
Doctorate
University:
University of Baghdad - Faculty Of Administration And Economics - Department Of Statistics
Language:
Arabic
University location:
Baghdad
First pages:
07T3754 - p.pdf
Abstract:
تعاني بعض الظواهر من وجود حالة اضطراب في بياناتها بالاضافة الى صعوبة صياغتها وخصوصا مع وجود عدم وضوح في استجابتها، او لكثرة الاختلافات الجوهرية التي تعتري الوحدات التجريبية التي تم اخذ هذه البيانات منها. فعندما تزداد المجاميع لمجتمع ما, لا يمكن اعتبار ذل | Often suffer phenomena and there is disturbances in their data as well as the difficulty of formulation, especially with a lack of clarity in the response, or the large number of essential differences plaguing the experimental units that have been taking this data from them. When growing groups of a population, it can't be regarded as a homogeneous population is increasing because the dispersion between the experimental units in response to that population as well as the error will also be increasingly difficult to build a model of that population.So there was a need to include an estimation method on the classification or underlying discrimination for these pilot units using discrimination or create segments for each item of these pilot units in the hope of controlling their responses and make it more in line style.Has been resorting to the use of the method of a modern and flexible classification is based on the neighborhoods between the views represented by the response variable by classification regression trees : CART. It was the use bayesian approach and the promise of features indicative of regression analysis model classification tree to take advantage of the above information by, and collect trees for explanatory variables are all together and at every stage fork on the other hand, represented the bayesian classification regression trees : BART.In view of the development in the field of computers and taking the principle of the integration of science it has been found that modern algorithms used in the field of Computer Science swarm of birds and other originally illustrated algorithm for the purposes of technology pertaining to distinguish between images or vibrational radio, etc., can be harnessed to serve the knowledge of statistics, which is based Turn on the information production and to achieve success in it. So is the use of genetic algorithm and submit a proposal by modified genetic algorithm, in addition to C4.5 and ID3 algorithms. And without losing the generality has been used as a tool of logistic analysis as important tool.It was a whole performance along with an estimate of the general additive model method of classification of all the methods mentioned in the above. Fitted model as modern and flexible overcomes curse of dimensionality that may occur most modern phenomena of the times when their data analysis.It was the use of simulated samples of different sizes (200, 400, 600) with (1000) replicates. The application was performed on patients with diabetes data for those aged (15) years and below are taken from the sample size (200) was withdrawn from the children hospital/ Al - Eskan/ Baghdad.As proof of the goodness of classification has been using the misclassification error, and as proof of the goodness of estimation has been using the root mean squares error.Was reached important conclusions through a process of differentiation among the six methods above, the superiority of the Classification and Regression Trees Algorithm (CART) is the best in terms of the results that has been reached in empirical side, when proposed Genetic Algorithm is the best in terms of the results that has been reached in applied side.
