On the power of Chatterjee’s rank correlation

Recently, Chatterjee (2020) introduced a new rank correlation that attracts many statisticians’ attention. This paper compares it to three already well-used rank correlations in literature, Hoeffding’s D, Blum–Kiefer–Rosenblatt’s R, and Bergsma–Dassios–Yanagimoto’s τ. Three criteria are considered: (i) computational efficiency, (ii) consistency against fixed alternatives, and (iii) power against local alternatives. Our main results show the unfortunate rate sub-optimality of Chatterjee’s rank correlation against three popular local alternatives in independence testing literature. Along with some recent computational breakthroughs, they favor the other three in many settings.

[1]  Chaim Even-Zohar,et al.  Counting Small Permutation Patterns , 2019, SODA.

[2]  Bodhisattva Sen,et al.  Multivariate Rank-Based Distribution-Free Nonparametric Testing Using Measure Transportation , 2019, Journal of the American Statistical Association.

[3]  M. Drton,et al.  Rate-Optimality of Consistent Distribution-Free Tests of Independence Based on Center-Outward Ranks and Signs , 2020 .

[4]  S. Chatterjee A New Coefficient of Correlation , 2019, Journal of the American Statistical Association.

[5]  M. Drton,et al.  Distribution-Free Consistent Independence Tests via Center-Outward Ranks and Signs , 2019, Journal of the American Statistical Association.

[6]  M. Drton,et al.  High-dimensional consistent independence testing with maxima of rank correlations , 2018, The Annals of Statistics.

[7]  N. Meinshausen,et al.  Symmetric rank covariances: a generalized framework for nonparametric measures of dependence , 2017, Biometrika.

[8]  R. Heller,et al.  Computing the Bergsma Dassios sign-covariance , 2016, 1605.08732.

[9]  Wicher P. Bergsma,et al.  A study of the power and robustness of a new test for independence against contiguous alternatives , 2016 .

[10]  Luca Weihs,et al.  Large-Sample Theory for the Bergsma-Dassios Sign Covariance , 2016, 1602.04387.

[11]  Mathias Drton,et al.  Efficient computation of the Bergsma–Dassios sign covariance , 2015, Comput. Stat..

[12]  Ursula Faber,et al.  Theory Of U Statistics , 2016 .

[13]  J. E. García,et al.  A non-parametric test of independence ∗ , 2011 .

[14]  C. Spearman The proof and measurement of association between two things. , 2015, International journal of epidemiology.

[15]  Wicher P. Bergsma,et al.  A consistent test of independence based on a sign covariance related to Kendall's tau , 2010, 1007.4259.

[16]  Alexandre B. Tsybakov,et al.  Introduction to Nonparametric Estimation , 2008, Springer series in statistics.

[17]  W. Kössler,et al.  The Asymptotic Efficacies and Relative Efficiencies of Various Linear Rank Tests for Independence , 2006 .

[18]  A. Barron,et al.  Fisher information inequalities and the central limit theorem , 2001, math/0111020.

[19]  J. Angus A Coupling Proof of the Asymptotic Normality of the Permutation Oscillation , 1995, Probability in the Engineering and Informational Sciences.

[20]  Y. Nikitin,et al.  Asymptotic Efficiency of Nonparametric Tests , 1995 .

[21]  Stefun D. Leigh U-Statistics Theory and Practice , 1992 .

[22]  Ing Rj Ser Approximation Theorems of Mathematical Statistics , 1980 .

[23]  T. Yanagimoto On measures of association and a related problem , 1970 .

[24]  G. Reuter LINEAR OPERATORS PART II (SPECTRAL THEORY) , 1969 .

[25]  P. Sen,et al.  Theory of rank tests , 1969 .

[26]  Corrado Gini L'ammontare e la Composizione Della Ricchezza Delle Nazioni , 1963 .

[27]  J. Kiefer,et al.  DISTRIBUTION FREE TESTS OF INDEPENDENCE BASED ON THE SAMPLE DISTRIBUTION FUNCTION , 1961 .

[28]  D. Farlie The Asymptotic Efficiency of Daniels's Generalized Correlation Coefficients , 1961 .

[29]  D. J. G. Farlie,et al.  The performance of some correlation coefficients for a general bivariate distribution , 1960 .

[30]  Steven Orey,et al.  A central limit theorem for $m$-dependent random variables , 1958 .

[31]  H. S. Konijn On the Power of Certain Tests for Independence in Bivariate Populations , 1956 .

[32]  F. Smithies Linear Operators , 2019, Nature.

[33]  Nils Blomqvist,et al.  On a Measure of Dependence Between two Random Variables , 1950 .

[34]  M. Kendall A NEW MEASURE OF RANK CORRELATION , 1938 .

[35]  C. Spearman ‘FOOTRULE’ FOR MEASURING CORRELATION , 1906 .