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Article: Asymptotic mean and variance of gini correlation for bivariate normal samples

TitleAsymptotic mean and variance of gini correlation for bivariate normal samples
Authors
KeywordsAsymptotic relative efficiency
Bivariate normal
Correlation coefficient
In-between
Non-linearity
Issue Date2010
PublisherIEEE.
Citation
Ieee Transactions On Signal Processing, 2010, v. 58 n. 2, p. 522-534 How to Cite?
AbstractThis paper derives the asymptotic analytical forms of the mean and variance of the Gini correlation (GC) with respect to samples drawn from bivariate normal populations. The asymptotic relative efficiency (ARE) of the Gini correlation to Pearson's product moment correlation coefficient (PPMCC) is investigated under the normal assumptions. To gain further insight into GC, we also compare the Gini correlation to other two closely related correlation coefficients, namely, the order statistics correlation coefficient (OSCC) and Spearman's rho (SR). Theoretical and simulation results suggest that the performance of GC lies in between those of OSCC and SR when estimating the correlation coefficient of the bivariate normal population. The newly found theoretical results along with other desirable properties enable GC to be a useful alternative to the existing coefficients, especially when one wants to make a trade-off between the efficiency and robustness to monotone nonlinearity. Copyright © 2010 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/124699
ISSN
2015 Impact Factor: 2.624
2015 SCImago Journal Rankings: 2.004
ISI Accession Number ID
Funding AgencyGrant Number
University of Hong Kong200807176233
Funding Information:

Manuscript received February 06, 2009; accepted August 24, 2009. First published September 18, 2009; current version published January 13, 2010. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Philippe Ciblat. This work was supported in part by the University of Hong Kong under Small Project Grant 200807176233.

References

 

DC FieldValueLanguage
dc.contributor.authorXu, Wen_HK
dc.contributor.authorHung, YSen_HK
dc.contributor.authorNiranjan, Men_HK
dc.contributor.authorShen, Men_HK
dc.date.accessioned2010-10-31T10:49:11Z-
dc.date.available2010-10-31T10:49:11Z-
dc.date.issued2010en_HK
dc.identifier.citationIeee Transactions On Signal Processing, 2010, v. 58 n. 2, p. 522-534en_HK
dc.identifier.issn1053-587Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/124699-
dc.description.abstractThis paper derives the asymptotic analytical forms of the mean and variance of the Gini correlation (GC) with respect to samples drawn from bivariate normal populations. The asymptotic relative efficiency (ARE) of the Gini correlation to Pearson's product moment correlation coefficient (PPMCC) is investigated under the normal assumptions. To gain further insight into GC, we also compare the Gini correlation to other two closely related correlation coefficients, namely, the order statistics correlation coefficient (OSCC) and Spearman's rho (SR). Theoretical and simulation results suggest that the performance of GC lies in between those of OSCC and SR when estimating the correlation coefficient of the bivariate normal population. The newly found theoretical results along with other desirable properties enable GC to be a useful alternative to the existing coefficients, especially when one wants to make a trade-off between the efficiency and robustness to monotone nonlinearity. Copyright © 2010 IEEE.en_HK
dc.languageengen_HK
dc.publisherIEEE.-
dc.relation.ispartofIEEE Transactions on Signal Processingen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsIEEE Transactions on Signal Processing. Copyright © IEEE.-
dc.rights©2010 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.subjectAsymptotic relative efficiency-
dc.subjectBivariate normal-
dc.subjectCorrelation coefficient-
dc.subjectIn-between-
dc.subjectNon-linearity-
dc.titleAsymptotic mean and variance of gini correlation for bivariate normal samplesen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1053-587X&volume=58&issue=2&spage=522&epage=534&date=2010&atitle=Asymptotic+mean+and+variance+of+gini+correlation+for+bivariate+normal+samples-
dc.identifier.emailXu, W: wcxu@eee.hku.hken_HK
dc.identifier.emailHung, YS: yshung@hkucc.hku.hken_HK
dc.identifier.authorityXu, W=rp00198en_HK
dc.identifier.authorityHung, YS=rp00220en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/TSP.2009.2032448en_HK
dc.identifier.scopuseid_2-s2.0-74949103910en_HK
dc.identifier.hkuros175046en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-74949103910&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume58en_HK
dc.identifier.issue2en_HK
dc.identifier.spage522en_HK
dc.identifier.epage534en_HK
dc.identifier.isiWOS:000273609000005-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridXu, W=7404428876en_HK
dc.identifier.scopusauthoridHung, YS=8091656200en_HK
dc.identifier.scopusauthoridNiranjan, M=7003348263en_HK
dc.identifier.scopusauthoridShen, M=7401466148en_HK

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