Statistical properties of a kernel-type estimator of the intensity function of a cyclic Poisson process

被引：11
|
作者：
Helmers, R
Mangku, IW
Zitikis, R [1 ]
机构：
[1] Univ Western Ontario, Dept Stat & Actuarial Sci, London, ON N6A 5B7, Canada
[2] Ctr Math & Comp Sci CWI, NL-1090 GB Amsterdam, Netherlands
[3] Bogor Agr Univ, Dept Math, Bogor 16144, Indonesia
来源：
JOURNAL OF MULTIVARIATE ANALYSIS | 2005年 / 92卷 / 01期
基金：
加拿大自然科学与工程研究理事会;
关键词：
Poisson process; point process; intensity function; period; nonparametric estimation; consistency; bias; variance; mean-squared error;
D O I：
10.1016/S0047-259X(03)00082-4
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：
We consider a kernel-type nonparametric estimator of the intensity function of a cyclic Poisson process when the period is unknown. We assume that only a single realization of the Poisson process is observed in a bounded window which expands in time. We compute the asymptotic bias, variance, and the mean-squared error of the estimator when the window indefinitely expands. (C) 2003 Elsevier Science (USA). All rights reserved.
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页码：1 / 23
页数：23
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