Asymptotic properties of mean cumulative function estimators from window-observation recurrence data

被引:2
|
作者
Zuo, Jianying [1 ]
Wu, Huaiqing [1 ]
Meeker, William Q. [1 ]
机构
[1] Iowa State Univ Sci & Technol, Dept Stat, Ames, IA 50011 USA
关键词
Asymptotic normality; Consistency; Nonhomogeneous Poisson process; Nonparametric estimation;
D O I
10.1016/j.jspi.2012.04.010
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
A variety of nonparametric and parametric methods have been used to estimate the mean cumulative function (MCF) for the recurrence data collected from the counting process. When the recurrence histories of some units are available in disconnected observation windows with gaps in between, Zuo et al. (2008) showed that both the nonparametric and parametric methods can be extended to estimate the MCF. In this article, we establish some asymptotic properties of the MCF estimators for the window-observation recurrence data. (C) 2012 Elsevier B.V. All rights reserved.
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页码:2943 / 2952
页数:10
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