BOOTSTRAP WAVELET IN THE NONPARAMETRIC REGRESSION MODEL WITH WEAKLY DEPENDENT PROCESSES

被引：0
|
作者：
林路
张润楚
机构：
[1] School of Mathematics and system Scences
[2] shandong University
[3] Jinan 250100
[4] China School of Mathematical Sciences
[5] Nankai University
[6] Tianjin 300071. China
[7] School of Mathematics and System Science
[8] Shandong University
[9] Jinan 250100
来源：
Acta Mathematica Scientia | 2004年 / 01期
关键词：
Nonparametric regression; weakly dependent process; bootstrap; wavelet;
D O I：
暂无
中图分类号：
O241 [数值分析];
学科分类号：
070102 ;
摘要：
This paper introduces a method of bootstrap wavelet estimation in a non-parametric regression model with weakly dependent processes for both fixed and random designs. The asymptotic bounds for the bias and variance of the bootstrap wavelet estimators are given in the fixed design model. The conditional normality for a modified version of the bootstrap wavelet estimators is obtained in the fixed model. The consistency for the bootstrap wavelet estimator is also proved in the random design model. These results show that the bootstrap wavelet method is valid for the model with weakly dependent processes.
引用
收藏
页码：61 / 70
页数：10
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