Nonparametric synthetic data regression estimation for censored survival data

被引：6

作者：

Singh, RS ^{[1
]}

Lu, XW ^{[1
]}

机构：

[1] Univ Guelph, Dept Math & Stat, Guelph, ON N1G 2W1, Canada

来源：

JOURNAL OF NONPARAMETRIC STATISTICS | 1999年 / 11卷 / 1-3期

关键词：

survival analysis; synthetic data; kernel smoothing; counting process; asymptotic normality;

D O I：

10.1080/10485259908832773

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：

This paper studies the large sample properties of the censored data nonparametric regression estimation in multivariate case. The data transformation is derived from the synthetic data method proposed by Leurgans. The asymptotic distributions are derived by counting process techniques. The results are then considered as an extension of the kernel smoothing for the complete data.

引用

页码：13 / 31

页数：19

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