Parameters behind "nonparametric" statistics: Kendall's tau, Somers' D and median differences
Parameters behind "nonparametric" statistics: Kendall's tau, Somers' D and median differences
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DOI：
10.1177/1536867x0200200103
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发表时间：
2002-03-01
期刊：
影响因子：
4.8
通讯作者：
Newson, Roger
中科院分区：
文献类型：
--
作者：
Newson, Roger
所谓的“非参数”统计方法实际上往往是以总体参数为基础的，这些参数可以使用相应的样本统计数据来估计(有置信限)。本文回顾了三个这样的参数的应用，即Kendall‘s tau(α)，Somers’s D和Hodges-Lehmann中位数差。这些参数的可信区间是使用smersd程序包演示的。有人认为，这些参数的可信度及其差异比只报告p值的传统做法更具信息性。这三个参数在确定其他测试和参数时也很重要，例如Wilcoxon检验、受试者工作特性(ROC)曲线下的面积、Harrell‘s C和泰尔中位数斜率。
So-called "nonparametric" statistical methods are often in fact based on population parameters, which can be estimated (with confidence limits) using the corresponding sample statistics. This article reviews the uses of three such parameters, namely Kendall's tau(alpha), Somers' D and the Hodges-Lehmann median difference. Confidence intervals for these are demonstrated using the somersd package. It is argued that confidence limits for these parameters, and their differences, are more informative than the traditional practice of reporting only p-values. These three parameters are also important in defining other tests and parameters, such as the Wilcoxon test, the area under the receiver operating characteristic (ROC) curve, Harrell's C, and the Theil median slope.