Kernel estimation of the conditional mode for fixed design models

العناوين الأخرى

تقدير النواة للمنوال الشرطي لنماذج التصميم الثابتة

مقدم أطروحة جامعية

al-Hartani, Dua Abd al-Rahman Khalil

مشرف أطروحة جامعية

Salihah, Raid Bashir Ibrahim

الجامعة

الجامعة الإسلامية

الكلية

كلية العلوم

القسم الأكاديمي

قسم الرياضيات

دولة الجامعة

فلسطين (قطاع غزة)

الدرجة العلمية

ماجستير

تاريخ الدرجة العلمية

2017

الملخص الإنجليزي

We are interested in the area of nonparametric prediction, therefore, we study the relationship between a current observation and previous observations, where the conditional density function plays an important role.

In this thesis, we study the problem of estimating nonparametrically the conditional mode for fixed design models.

We suppose the error random variables are independent.

The joint asymptotic normality of the conditional mode estimator at different fixed design points is established under some regularity conditions.

We started our study by consider the mode of a sample of identically independent distributed (i.i.d.) data.

Also, we study some theoretical properties of the kernel estimator for the conditional density function.

Moreover we study some sufficient conditions under which the kernel estimation of the mode for random design models is asymptotically normally distributed.

After that we study the kernel estimation of the mode for fixed design models.

We assume that (x1, Y1),(x2, Y2), ...,(xn, Yn) are i.i.d.

random variables with conditional pdf f(y|x) where the variable Y depending upon a fixed design predictor x through a regression function m(x).

Finally, we used the proposed estimator in some applications using simulated and real data.

The results have indicated that the mode estimator has a good performance and efficiency estimator by MSE error and correlation coefficient.

التخصصات الرئيسية

الرياضيات

عدد الصفحات

84

قائمة المحتويات

• APA
• MLA
• AMA

لغة النص

الإنجليزية

نوع البيانات

رسائل جامعية

رقم السجل

BIM-901169