On Rank Correlation Coefficients

In the present paper, we propose a new rank correlation coefficient $r_n$, which is a sample analogue of the theoretical correlation coefficient $r$, which, in turn, was proposed in the recent work of Stepanov (2025b). We discuss the properties of $r_n$ and compare $r_n$ with known rank Spearman ${\rho }_{S,n}$, Kendall ${\tau }_n$ and sample Pearson ${\rho }_n$ correlation coefficients. Simulation experiments show that when the relationship between $X$ and $Y$ is not close to linear, $r_n$ performs better than other correlation coefficients. We also find analytically the values of $Var({\tau }_n)$ and $Var(r_n)$. This allows to estimate theoretically the asymptotic performance of ${\tau }_n$ and $r_n$.