Asymptotic Properties of Order Statistics Correlation Coefficient in the Normal Cases

Authors:
Weichao Xu;Chunqi Chang;Y.S. Hung;P.C.W. Fung
Affiliations:
Dept. of Electr. & Electron. Eng., Hong Kong Univ., Hong Kong;-;-;-
Venue:
IEEE Transactions on Signal Processing
Year:
2008

Citing 0
Cited 3

Asymptotic mean and variance of Gini correlation for bivariate normal samples

IEEE Transactions on Signal Processing
A comparative analysis of Spearman's rho and Kendall's tau in normal and contaminated normal models

Signal Processing
Estimating the area under a receiver operating characteristic (ROC) curve: Parametric and nonparametric ways

Signal Processing

Quantified Score

Hi-index	35.69

Visualization

Abstract

We have previously proposed a novel order statistics correlation coefficient (OSCC), which possesses some desirable advantages when measuring linear and monotone nonlinear associations between two signals. However, the understanding of this new coefficient is far from complete. A lot of theoretical questions, such as the expressions of its distribution and moments, remain to be addressed. Motivated by this unsatisfactory situation, in this paper we prove that for samples drawn from bivariate normal populations, the distribution of OSCC is asymptotically equivalent to that of the Pearson's product moment correlation coefficient (PPMCC). We also reveal its close relationships with the other two coefficients, namely, Gini correlation (GC) and Spearman's rho (SR). Monte Carlo simulation results agree with the theoretical findings.