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Conference Paper: On estimation of the noise variance in a high-dimensional signal detection model

TitleOn estimation of the noise variance in a high-dimensional signal detection model
Authors
KeywordsHigh-dimensional signal detection
Noise variance estimator
Random matrix theory
Issue Date2014
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001269
Citation
The 2014 IEEE Workshop on Statistical Signal Processing (SSP), Gold Coast, Australia, 29 June-2 July 2014. In Conference Proceedings, 2014, p. 17-20 How to Cite?
AbstractWhen the number of receivers p is large compared to the sample size n, it has been widely observed that standard inference solutions are no longer efficient. In this paper, we address such high-dimensional issues related to the estimation of the noise variance. Several authors have reported that the classical maximum likelihood estimator of the noise variance tends to have a downward bias and this bias is increasingly important when p increases. Using recent results of random matrix theory, we are able to identify the bias. Moreover, a bias-corrected estimator is proposed using this knowledge. The asymptotic normality of the estimator in the high-dimensional context is established.
Persistent Identifierhttp://hdl.handle.net/10722/201429
ISBN

 

DC FieldValueLanguage
dc.contributor.authorYao, JJen_US
dc.contributor.authorPassemier, Den_US
dc.date.accessioned2014-08-21T07:27:21Z-
dc.date.available2014-08-21T07:27:21Z-
dc.date.issued2014en_US
dc.identifier.citationThe 2014 IEEE Workshop on Statistical Signal Processing (SSP), Gold Coast, Australia, 29 June-2 July 2014. In Conference Proceedings, 2014, p. 17-20en_US
dc.identifier.isbn978-1-4799-4975-5-
dc.identifier.urihttp://hdl.handle.net/10722/201429-
dc.description.abstractWhen the number of receivers p is large compared to the sample size n, it has been widely observed that standard inference solutions are no longer efficient. In this paper, we address such high-dimensional issues related to the estimation of the noise variance. Several authors have reported that the classical maximum likelihood estimator of the noise variance tends to have a downward bias and this bias is increasingly important when p increases. Using recent results of random matrix theory, we are able to identify the bias. Moreover, a bias-corrected estimator is proposed using this knowledge. The asymptotic normality of the estimator in the high-dimensional context is established.-
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001269-
dc.relation.ispartofIEEE/SP Workshop on Statistical Signal Processing Proceedingsen_US
dc.rightsIEEE/SP Workshop on Statistical Signal Processing Proceedings. Copyright © IEEE.-
dc.rights©2014 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectHigh-dimensional signal detection-
dc.subjectNoise variance estimator-
dc.subjectRandom matrix theory-
dc.titleOn estimation of the noise variance in a high-dimensional signal detection modelen_US
dc.typeConference_Paperen_US
dc.identifier.emailYao, JJ: jeffyao@hku.hken_US
dc.identifier.authorityYao, JJ=rp01473en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/SSP.2014.6884564-
dc.identifier.hkuros233582en_US
dc.identifier.spage17en_US
dc.identifier.epage20en_US
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 140903-

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