Kendall's tau is a nonparametric measure of correlation. We present an efficient method for computing the empirical estimate of Kendall's tau and the jackknife estimate of its variance. For datasets of fixed dimension, the algorithm's runtime is log-linear in the number of observations. This is achieved by modifying the standard algorithm for computing the empirical Kendall's tau to return estimates of the summands making up its H\'{a}jek projection.

翻译：Kendall 的 Tau 是一个非参数性的相关度量。 我们提出了一个有效的方法来计算 Kendall 的 Tau 和 jacknife 的实验估计值及其差异。 对于固定尺寸的数据集来说, 算法的运行时间是观测次数的日志线。 这是通过修改计算经验性 Kendall 的 tau 的标准算法来实现的, 以返回构成其 H\ {a}jek 预测的总和的估算值。