Matrix analysis
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
IEEE Transactions on Signal Processing
A Nonlinear Stein-Based Estimator for Multichannel Image Denoising
IEEE Transactions on Signal Processing - Part II
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
A radar application of a modified Cramer-Rao bound: parameterestimation in non-Gaussian clutter
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing - Part I
Radar Detection and Classification of Jamming Signals Belonging to a Cone Class
IEEE Transactions on Signal Processing
Asymptotic Properties of Order Statistics Correlation Coefficient in the Normal Cases
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing - Part I
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Lower bounds on sample size in structural equation modeling
Electronic Commerce Research and Applications
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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.