Block thresholding and wavelet estimation for nonequispaced samples

被引：20

作者：

Chicken, E ^{[1
]}

机构：

[1] Purdue Univ, Dept Stat, W Lafayette, IN 47907 USA

来源：

JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2003年 / 116卷 / 01期

关键词：

wavelets; nonparametric regression; adaptive; Besov class; Holder class; Poisson process;

D O I：

10.1016/S0378-3758(02)00238-0

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：

For samples with the design points occurring as a Poisson process or having a uniform distribution, the wavelet method of block thresholding can be applied directly to the data as though it was equispaced without sacrificing adaptivity or optimality. When the underlying true function is in certain Besov and Holder classes, the resulting estimator achieves the minimax rate of convergence. Simulation results are examined. (C) 2002 Elsevier B.V. All rights reserved.

引用

页码：113 / 129

页数：17

共 10 条

[1] Lower bounds on the rate of convergence of nonparametric regression estimates [J].