Bandwidth Selection for Kernel Quantile Estimation
Resource
中國統計學報 44(3),271-295
Journal
中國統計學報,44
Pages
271-295
Date Issued
2006
Date
2006
Author(s)
Sun, Shan
DOI
20060927121124507898
Abstract
In 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.
Subjects
Bandwidth
kernel
quantile
nonparametric smoothing.
Publisher
臺北市:國立臺灣大學數學系
Type
journal article
File(s)![Thumbnail Image]()
Loading...
Name
pohq4.pdf
Size
851.7 KB
Format
Adobe PDF
Checksum
(MD5):3e95733b1f9ad4d20d95c2ece34e6d93