testing independence
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2022 ◽  
Vol 217 ◽  
pp. 188-203
Author(s):  
Fanrong Zhao ◽  
Nan Lin ◽  
Wenjuan Hu ◽  
Baoxue Zhang

Biometrika ◽  
2021 ◽  
Author(s):  
H Shi ◽  
M Drton ◽  
F Han

Abstract Chatterjee (2021+) introduced a simple new rank correlation coefficient that has attracted much recent attention. The coefficient has the unusual appeal that it not only estimates a population quantity first proposed by Dette et al. (2013) that is zero if and only if the underlying pair of random variables is independent, but also is asymptotically normal under independence. This paper compares Chatterjee’s new correlation coefficient to three established rank correlations that also facilitate consistent tests of independence, namely, Hoeffding’s D, Blum–Kiefer– Rosenblatt’s R, and Bergsma–Dassios–Yanagimoto’s τ *. We contrast their computational efficiency in light of recent advances, and investigate their power against local rotation and mixture alternatives. Our main results show that Chatterjee’s coefficient is unfortunately rate sub-optimal compared to D, R, and τ *. The situation is more subtle for a related earlier estimator of Dette et al. (2013). These results favor D, R, and τ * over Chatterjee’s new correlation coefficient for the purpose of testing independence.


PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249804
Author(s):  
Mohammed Elhenawy ◽  
Sébastien Glaser ◽  
Andy Bond ◽  
Andry Rakotonirainy ◽  
Sébastien Demmel ◽  
...  

2021 ◽  
Vol 182 ◽  
pp. 104711
Author(s):  
Tingyu Lai ◽  
Zhongzhan Zhang ◽  
Yafei Wang ◽  
Linglong Kong
Keyword(s):  

Author(s):  
Shih-Hao Huang ◽  
Hsin-Cheng Huang ◽  
Ruey S. Tsay ◽  
Guangming Pan

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