Asymptotic mean and variance of Gini correlation for bivariate normal samples

Xu, Weichao, Hung, Y.S., Niranjan, M. and Shen, M.F. (2009) Asymptotic mean and variance of Gini correlation for bivariate normal samples IEEE Transactions on Signal Processing, 58, (2), pp. 522-534. (doi:10.1109/TSP.2009.2032448).


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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.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1109/TSP.2009.2032448
Additional Information: Imported from ISI Web of Science
ISSNs: 1053-587X (print)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Organisations: Southampton Wireless Group
ePrint ID: 270723
Date :
Date Event
18 September 2009e-pub ahead of print
February 2010Published
Date Deposited: 21 Apr 2010 07:46
Last Modified: 17 Apr 2017 18:28
Further Information:Google Scholar

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