Deconvolution model with fractional Gaussian noise: A minimax study

Rida Benhaddou

Statistics & Probability Letters, 2016, vol. 117, issue C, 201-208

Abstract: We consider the problem of estimating a function in a deconvolution model with fractional Gaussian noise. We derive minimax lower and upper bounds to show that our estimator attains optimal or near optimal rates. Such rates are affected by LRD.

Keywords: Deconvolution; Fractional Gaussian noise; Minimax convergence rate (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167715216300815
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:117:y:2016:i:c:p:201-208

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.spl.2016.05.022

Access Statistics for this article

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().