Asymptotic Mean and Variance of Gini Correlation Under Contaminated Gaussian Model | Zendy

Rubao Ma | Zendy; Weichao Xu | Zendy; Shun Liu | Zendy; Yun Zhang | Zendy; Jianbin Xiong | Zendy

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Asymptotic Mean and Variance of Gini Correlation Under Contaminated Gaussian Model

Author(s) -

Rubao Ma,

Weichao Xu,

Shun Liu,

Yun Zhang,

Jianbin Xiong

Publication year - 2016

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2016.2622358

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

This paper establishes the asymptotic closed forms of the expectation and variance of the Gini correlation (GC) under a particular type of bivariate contaminated Gaussian model emulating a frequently encountered scenario in statistical signal processing. Monte Carlo simulation results verify the correctness of the theoretical results established in this paper. In order to gain further insight into GC, we also compare GC to Pearson's product moment correlation coefficient, Kendall's tau, and Spearman's rho by means of root mean squared error. The newly explored theoretical and simulational findings not only deepen the understanding of the rather new GC, but also shed new light on the topic of correlation theory, which is widely applied in statistical signal processing.

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