When dealing with ordinal correlations (e.g. Spearman's Rho, Kendall's Tau), one can non-parametrically test the null hypothesis of no correlation by a random permutation test (shuffling one of the two variables).
However, if we have a significantly non-zero correlation and we'd like to obtain a confidence interval for the correlation coefficient we need a standard error estimate that doesn't rely on the null hypothesis.
Bootstrapping seems to the way to go, but then comes the problem of duplicated observations. The bootstrapped samples contain duplicated observations due to resampling with replacement, and these duplicates generate ties that affect the ordinal correlations measures. For example, if one uses Kendall's Tau a, these spurious ties will decrease the correlation coefficient (i.e., introduce a negative bias).
How to deal with this problem? will a BCA (bias corrected and accelerated) confidence interval be appropriate here ? or alternatively, should we use a different resampling approach (e.g., jackknifing) instead?