Generalized least squares cross-validation in kernel density estimation

被引：4

作者：

Zhang, Jin ^{[1
]}

机构：

[1] Yunnan Univ, Sch Math & Stat, Kunming 650091, Yunnan, Peoples R China

来源：

STATISTICA NEERLANDICA | 2015年 / 69卷 / 03期

关键词：

bandwidth; integrated squared error; normal mixture; oversmoothing; undersmoothing; NORMAL REFERENCE BANDWIDTH; SELECTION;

D O I：

10.1111/stan.12061

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

The kernel density estimation is a popular method in density estimation. The main issue is bandwidth selection, which is a well-known topic and is still frustrating statisticians. A robust least squares cross-validation bandwidth is proposed, which significantly improves the classical least squares cross-validation bandwidth for its variability and undersmoothing, adapts to different kinds of densities, and outperforms the existing bandwidths in statistical literature and software.

引用

页码：315 / 328

页数：14

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