Laplace deconvolution in the presence of indirect long-memory data
Statistics Theory
2017-06-28 v1 Statistics Theory
Abstract
We investigate the problem of estimating a function based on observations from its noisy convolution when the noise exhibits long-range dependence. We construct an adaptive estimator based on the kernel method, derive minimax lower bound for the -risk when belongs to Sobolev space and show that such estimator attains optimal rates that deteriorate as the LRD worsens.
Keywords
Cite
@article{arxiv.1706.08648,
title = {Laplace deconvolution in the presence of indirect long-memory data},
author = {Rida Benhaddou},
journal= {arXiv preprint arXiv:1706.08648},
year = {2017}
}