الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
Nonparametric kernel estimators are widely used in many research areas of statistics.
An important nonparametric kernel estimators of the conditional quantiles
are the Nadaraya-Watson and Weighted Nadaraya-Watson kernel estimation of the
conditional quantiles which is often obtained by using a xed bandwidth. One of
the important issues in kernel smoothing is the choice of the smoothing parameters.
In this thesis, we propose a new method of smoothing for nonparametric conditional
quantile which depends on di erent bandwidths.
We consider the adaptive Nadaraya-Watson kernel estimation of the conditional
quantiles and the adaptive Weighted Nadaraya-Watson kernel estimation of the
conditional quantiles. The results of the simulation studies show that the adaptive
Nadaraya-Watson kernel estimation and the adaptive Weighted Nadaraya-Watson
estimation have better performance than the kernel estimations with xed bandwidths.
Key Words: Kernel estimation, quantile regression ,conditional quantiles, conditional
distribution, asymptotic normality, adaptive kernel estimates.