用第一类斯特林数的群体遗传统计分布函数:渐近、反演和数值计算

S. Chen, N. Temme
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引用次数: 0

摘要

第一类斯特林数在数论和组合学中很常见;通过Ewen的抽样公式,这些数字进入到几个群体遗传统计的计算中,比如Fu的f。在以前的论文中,我们考虑了有限Stirling数和的渐近估计量,它可以快速准确地计算Fu的f。这些总和也可以看作是一个累积分布函数;这个公式直接导致了一个反演问题,在这个问题中,给定Fu的f的一个值,目标是求解其中一个输入参数。我们用牛顿迭代法求解小参数反演。对于较大的参数,我们需要将之前得到的渐近结果进行推广,以渐近地处理反演问题。数值实验表明,该方法既能有效地解决反演问题,又能有效地对统计量进行扩展估计。
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A distribution function from population genetics statistics using Stirling numbers of the first kind: Asymptotics, inversion and numerical evaluation
Stirling numbers of the first kind are common in number theory and combinatorics; through Ewen's sampling formula, these numbers enter into the calculation of several population genetics statistics, such as Fu's Fs. In previous papers we have considered an asymptotic estimator for a finite sum of Stirling numbers, which enables rapid and accurate calculation of Fu's Fs. These sums can also be viewed as a cumulative distribution function; this formulation leads directly to an inversion problem, where, given a value for Fu's Fs, the goal is to solve for one of the input parameters. We solve this inversion using Newton iteration for small parameters. For large parameters we need to extend the earlier obtained asymptotic results to handle the inversion problem asymptotically. Numerical experiments are given to show the efficiency of both solving the inversion problem and the expanded estimator for the statistical quantities.
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