On a strongly consistent wavelet density estimator for the deconvolution problem

被引：2
|
作者：
Lee, S
Hong, DH
机构：
[1] Taegu Univ, Dept Stat, Taegu 712714, South Korea
[2] Catholic Univ Taegu, Sch Mech & Automot Engn, Taegu 712702, South Korea
来源：
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2002年 / 31卷 / 08期
关键词：
consistency; deconvolution; Meyer wavelet; Sobolev space;
D O I：
10.1081/STA-120006067
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：
The problem of wavelet density estimation is studied when the sample observations are contaminated with random noise. In this paper a linear wavelet estimator based on Meyer-type wavelets is shown to be strongly consistent when Fourier transform of random noise has polynomial descent or exponential descent.
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页码：1259 / 1272
页数：14
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