Minimax lower bounds for the simultaneous wavelet deconvolution with fractional Gaussian noise and unknown kernels

被引：3

作者：

Benhaddou, Rida ^{[1
]}

机构：

[1] Ohio Univ, Dept Math, Athens, OH 45701 USA

来源：

STATISTICS & PROBABILITY LETTERS | 2018年 / 140卷

关键词：

Simultaneous wavelet deconvolution; Blind deconvolution; Besov space; Long-range dependence; Minimax convergence rate; LINEAR INVERSE PROBLEMS; OPERATOR; ERROR;

D O I：

10.1016/j.spl.2018.05.002

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：

Lower bounds are constructed for estimators of a function in the simultaneous deconvolution with fractional Gaussian noise and unknown kernels, and are expressed as the maxima between two terms that represent the noise sources and their weakest Long-range dependence. (C) 2018 Elsevier B.V. All rights reserved.

引用

页码：91 / 95

页数：5

共 13 条

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[2] Minimax adaptive wavelet estimator for the anisotropic functional deconvolution model with unknown kernel
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