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.

引用

页码：1259 / 1272

页数：14

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