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Article: Asymptotic mean and variance of gini correlation for bivariate normal samples
Title | Asymptotic mean and variance of gini correlation for bivariate normal samples | ||||
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Authors | |||||
Keywords | Asymptotic relative efficiency Bivariate normal Correlation coefficient In-between Non-linearity | ||||
Issue Date | 2010 | ||||
Publisher | IEEE. | ||||
Citation | Ieee Transactions On Signal Processing, 2010, v. 58 n. 2, p. 522-534 How to Cite? | ||||
Abstract | This 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 Identifier | http://hdl.handle.net/10722/124699 | ||||
ISSN | 2023 Impact Factor: 4.6 2023 SCImago Journal Rankings: 2.520 | ||||
ISI Accession Number ID |
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 | |||||
Grants |
DC Field | Value | Language |
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dc.contributor.author | Xu, W | en_HK |
dc.contributor.author | Hung, YS | en_HK |
dc.contributor.author | Niranjan, M | en_HK |
dc.contributor.author | Shen, M | en_HK |
dc.date.accessioned | 2010-10-31T10:49:11Z | - |
dc.date.available | 2010-10-31T10:49:11Z | - |
dc.date.issued | 2010 | en_HK |
dc.identifier.citation | Ieee Transactions On Signal Processing, 2010, v. 58 n. 2, p. 522-534 | en_HK |
dc.identifier.issn | 1053-587X | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/124699 | - |
dc.description.abstract | This 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.language | eng | en_HK |
dc.publisher | IEEE. | - |
dc.relation.ispartof | IEEE Transactions on Signal Processing | en_HK |
dc.rights | ©2009 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.subject | Asymptotic relative efficiency | - |
dc.subject | Bivariate normal | - |
dc.subject | Correlation coefficient | - |
dc.subject | In-between | - |
dc.subject | Non-linearity | - |
dc.title | Asymptotic mean and variance of gini correlation for bivariate normal samples | en_HK |
dc.type | Article | en_HK |
dc.identifier.openurl | http://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.email | Xu, W: wcxu@eee.hku.hk | en_HK |
dc.identifier.email | Hung, YS: yshung@hkucc.hku.hk | en_HK |
dc.identifier.authority | Xu, W=rp00198 | en_HK |
dc.identifier.authority | Hung, YS=rp00220 | en_HK |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1109/TSP.2009.2032448 | en_HK |
dc.identifier.scopus | eid_2-s2.0-74949103910 | en_HK |
dc.identifier.hkuros | 175046 | en_HK |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-74949103910&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.volume | 58 | en_HK |
dc.identifier.issue | 2 | en_HK |
dc.identifier.spage | 522 | en_HK |
dc.identifier.epage | 534 | en_HK |
dc.identifier.isi | WOS:000273609000005 | - |
dc.publisher.place | United States | en_HK |
dc.relation.project | Quantifying Monotonic Association Among Multi-Channel Biosignals With A Novel Fast Concordance Coefficient | - |
dc.identifier.scopusauthorid | Xu, W=7404428876 | en_HK |
dc.identifier.scopusauthorid | Hung, YS=8091656200 | en_HK |
dc.identifier.scopusauthorid | Niranjan, M=7003348263 | en_HK |
dc.identifier.scopusauthorid | Shen, M=7401466148 | en_HK |
dc.identifier.issnl | 1053-587X | - |