论文标题
通过Edgeworth扩展的Fisher转换
Fisher transformation via Edgeworth expansion
论文作者
论文摘要
我们展示了如何计算Edgeworth系列的单个项，以借助一个简单的Mathematica程序近似Pearson相关系数的分布。我们还演示了如何消除相应的偏度，从而使近似更准确。这以一种相当自然的方式导致了Fisher Z变换的上级（准确性）版本。可以根据多元分布的随机独立样本来轻松修改该代码以处理定义为几种样本均值的任何样本统计信息。
We show how to calculate individual terms of the Edgeworth series to approximate the distribution of the Pearson correlation coefficient with the help of a simple Mathematica program. We also demonstrate how to eliminate the corresponding skewness, thus making the approximation substantially more accurate. This leads, in a rather natural way, to deriving a superior (in terms of its accuracy) version of Fisher's z transformation. The code can be easily modified to deal with any sample statistics defined as a function of several sample means, based on a random independent sample from a multivariate distribution.
