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مقارنة اساليب بيز مع طرائق اخرى لتقدير منحنى الانحدار اللامعلمي == A Comparison of Bayesian Approaches With Other Methods For Estimating Nonparametric Regression Curve
Author name:
خلود يوسف خمو يوسف
Supervisor name:
ظافر حسين رشيد النجار
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:
07T3610 - p.pdf
Abstract:
لقد شهدت طرائق الانحدار اللامعلمي اهتماما ملحوظا في السنوات الاخيرة كان السبب في ذلك هو ان التفكير المعلمي الصرف المستخدم في تقدير منحنى الانحدار لا يتوافق مع المرونة في تحليل البيانات.ومع التطور المنجز في اجهزة الحواسيب من الناحية المادية وكذلك انجاز | In recent years, too many considerations have been given for the Nonparametric Regression methods. This is for the reason that the concept of pure parametric; used for the estimation of Regression curve, does not cope well with the flexibility needed for data analysis.With the progress made in computer machines, in terms of economy and running performance, it has become possible to develop many of Nonparametric Regression methods theoretically. Though many of these are still under perfection, and facing a number of problems.Hence we see the importance of focusing on methods related to smoothing of Nonparametric Regression functions. This is for the purpose of producing the best methods convenient for various models. And for the Distribution Random error, in its two cases; Normal and Contaminated. Thus, the most important purpose of the research, is to find what the studies so far, have offered in the field of Nonparametric Regression. Also to find alternative or modified methods; which are reliable for the treatment of conditions of failure regarding the methods in use, as well as to alleviate the complexity of some methods, especially those related to Bayesian procedures.One of the most outstanding aims of the research focuses on the study of Nonparametric Regression using Bayesian variable selection. This suggests a modified technique to be reliable and of less complexity than the original one.Amongst the other research intentions, when data are contaminated with outliers, is to explore the Robust Nonparametric Regression, using Bayesian variable selection method. Also to suggest a modified Bayesian method; resistant to outliers, and of less complexity than before transformation. As well as offering suggested methods for Robust Nonparametric Regression. This is of the feature of having less sensitivity towards outliers and reliable in comparison to very few techniques supplied with robustness, as the Bayesian approach.A simulation model has been performed with different distributions, for the random error and for a number of models.To verify the performance of such methods, many criteria have been carried out.To satisfy the purpose of this research, the study has been divided into five chapters. Chapter I consists of an introduction, the problem under research, its importance and purposes. It also covers a literature survey. Chapter II covers methods for smoothing Nonparametric Regression. While chapter III is devoted for Nonparametric Regression and Robust Nonparametric Regression using Bayesian variable selection method. Also in this chapter are details of the suggested methods. Then chapter IV implements the experimental part of the study. Finally chapter V comprises the conclusions and suggestions that the research has recommended. As well as the future studies, which have been proposed regarding this research.
