Nonparametric estimation of quantile density function for truncated and censored data

被引：5

作者：

Zhou, Y ^{[1
]}

Yip, PSF

机构：

[1] Univ Hong Kong, Dept Stat, Hong Kong, Hong Kong, Peoples R China

[2] Acad Sinica, Inst Appl Math, Beijing 100080, Peoples R China

来源：

JOURNAL OF NONPARAMETRIC STATISTICS | 1999年 / 12卷 / 01期

关键词：

quantile density function; truncating and censoring; kernel estimator; random bandwidth; nearest neighbor estimator; optimal bandwidth;

D O I：

10.1080/10485259908832796

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：

In this paper we investigate the asymptotic properties of two types of kernel estimators for the quantile density function when the data are both randomly censored and truncated. We derive some laws of the logarithm for the maximal deviation between fixed bandwidth kernel estimators or random bandwidth kernel estimators and the true underlying quantile density function. Extensions to higher derivatives are included. The results are used to obtain the optimal bandwidth with respect to almost sure uniform convergence.

引用

页码：17 / 39

页数：23

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