Asymptotic properties of Pearson's rank-variate correlation coefficient under contaminated Gaussian model.
This paper investigates the robustness properties of Pearson's rank-variate correlation coefficient (PRVCC) in scenarios where one channel is corrupted by impulsive noise and the other is impulsive noise-free. As shown in our previous work, these scenarios that frequently encountered in radar and/or sonar, can be well emulated by a particular bivariate contaminated Gaussian model (CGM). Under this CGM, we establish the asymptotic closed forms of the expectation and variance of PRVCC by means of the well known Delta method. To gain a deeper understanding, we also compare PRVCC with two other classical correlation coefficients, i.e., Spearman's rho (SR) and Kendall's tau (KT), in terms of the root mean squared error (RMSE). Monte Carlo simulations not only verify our theoretical findings, but also reveal the advantage of PRVCC by an example of estimating the time delay in the particular impulsive noise environment.
Department of Automatic Control, School of Automation, Guangdong University of Technology, Guangzhou, Guangdong, China.
Rubao
R
Ma
Department of Automatic Control, School of Automation, Guangdong University of Technology, Guangzhou, Guangdong, China.
Weichao
W
Xu
Department of Automatic Control, School of Automation, Guangdong University of Technology, Guangzhou, Guangdong, China.
Yun
Y
Zhang
Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, Anhui, China.
Zhongfu
Z
Ye
D015233:Models, Statistical; D009010:Monte Carlo Method; D016011:Normal Distribution; D018709:Statistics, Nonparametric