A SAS Macro for Testing Differences among Three or More Independent Groups Using Kruskal-Wallis and Nemenyi Tests

As a nonparametric method,the Kruskal-Wallis test is widely used to compare three or more independent groups when an ordinal or interval level of data is available,especially when the assump-tions of analysis of variance (ANOVA) are not met.If the Kruskal-Wallis statistic is statistically signifi-cant,Nemenyi test is an alternative method for further pairwise multiple comparisons to locate the source of significance.Unfortunately,most popular statistical packages do not integrate the Nemenyi test,which is not easy to be calculated by hand.We described the theory and applications of the Kruskal-Wallis and Nemenyi tests,and presented a flexible SAS macro to implement the two tests.The SAS macro was demonstrated by two examples from our cohort study in occupational epidemiology.It provides a useful tool for SAS users to test the differences among three or more independent groups using a nonparametric method.

利用Stata编写ADO命令实现多样本多重比较的Nemenyi检验

目的利用Stata编写nemenyi.ado命令文件,从而实现Stata统计软件的非参数两两比较。方法利用Stata自带的do文件编辑器编写nemenyi.ado命令文件,根据已有文献实例进行演算说明使用方法。结果利用nemenyi.ado命令文件对实例数据分析,所得结果准确。结论本文所提供的ado命令文件与Stata统计软件的Kruskal-WallisH检验结果结合使用,可协助科研工作者进行类似数据分析。

功耗分析攻击中机器学习模型选择研究

根据密码芯片功耗曲线的特性,对支持向量机、随机森林、K最近邻、朴素贝叶斯4种机器学习算法进行分析研究,从中选择用于功耗分析攻击的最优算法。对于机器学习算法的数据选取问题,使用多组数量相同但组成元素不同的数据集的十折交叉验证结果进行模型选择,提高测试公平性及测试结果的泛化能力。为避免十折交叉验证过程中出现测试集误差不足以近似泛化误差的问题,采用Friedman检验及Nemenyi后续检验相结合的方法对4种机器学习算法进行评估,结果表明支持向量机是适用于功耗分析攻击的最优机器学习算法。