الفهرس | Only 14 pages are availabe for public view |
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 dierent 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. |