Arndt S, Magnotta V
Department of Psychiatry, Psychiatry Research - MEB, College of Medicine, University of Iowa, Iowa City, IA 52242, USA.
Comput Methods Programs Biomed. 2001 Apr;65(1):17-23. doi: 10.1016/s0169-2607(00)00102-4.
Kendall's tau(a) offers statistical advantages to the more common Pearson's correlation and both are common in biomedical research. While generating random X-Y pairs from a known population value of Pearson's correlation is straightforward, the process for generating random sequences for a known value of Kendall's tau(a) is more complicated. Algorithms are presented that yield random numbers from a population with a known expected tau(a). They begin with a small set of values that have a known tau. These values are 'grown' to produce an arbitrarily large population that has the same expectation as the smaller set. Two examples are given. One example simulated samples from a population where tau(a) equaled 0.33 and confidence intervals are produced. A second example illustrates how the algorithm can be used to provide statistical power estimates for research studies using Kendall's tau(a).
肯德尔tau(a) 相较于更常用的皮尔逊相关系数具有统计学优势,且二者在生物医学研究中都很常见。虽然从已知的皮尔逊相关系数总体值生成随机X - Y对很简单,但为已知的肯德尔tau(a) 值生成随机序列的过程则更为复杂。本文提出了一些算法,这些算法能从具有已知预期tau(a) 的总体中产生随机数。算法从一小组具有已知tau值的值开始。这些值会“扩展”,以产生一个与较小集合具有相同期望值的任意大的总体。文中给出了两个例子。一个例子是模拟来自tau(a) 等于0.33的总体的样本,并生成置信区间。第二个例子说明了该算法如何用于为使用肯德尔tau(a) 的研究提供统计功效估计。