题名 |
Bandwidth Selection for Kernel Quantile Estimation |
DOI |
10.29973/JCSA.200609.0004 |
作者 |
Ming-Yen Cheng;Shan Sun |
关键词 |
Bandwidth ; kernel ; quantile ; nonparametric smoothing |
期刊名称 |
中國統計學報 |
卷期/出版年月 |
44卷3期(2006 / 09 / 01) |
页次 |
271 - 295 |
内容语文 |
英文 |
英文摘要 |
In this article, we summarize some quantile estimators and related bandwidth selection methods and give two new bandwidth selection methods. By four distributions: standard normal, exponential, double exponential and log normal we simulated the methods and 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. |
主题分类 |
基礎與應用科學 >
統計 |
参考文献 |
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