國立臺灣大學數學系Cheng, Ming-YenMing-YenChengSun, ShanShanSun2006-09-272018-06-282006-09-272018-06-282006http://ntur.lib.ntu.edu.tw//handle/246246/20060927121124507898In this article, we summarize some quantile estimators & related bandwidth selection methods & give two new bandwidth selection methods. By four distributions: standard normal, exponential, double exponential & log normal we simulated the methods & compared their efficiencies to that of the empirical quantile. It turns out that kernel smoothed quantile estimators, with no matter which bandwidth selection method used, are more efficient than the empirical quantile estimator in most situations. And when sample size is relatively small, kernel smoothed estimators are especially more efficient than the empirical quantile estimator. However, no one method can beat any other methods for all distributions.application/pdf872141 bytesapplication/pdfzh-TWBandwidthkernelquantilenonparametric smoothing.journal articlehttp://ntur.lib.ntu.edu.tw/bitstream/246246/20060927121124507898/1/pohq4.pdf