Multivariate nonparametric tests
Multivariate nonparametric tests
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DOI：
10.1214/088342304000000558
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发表时间：
2004-11-01
影响因子：
5.7
通讯作者：
Randles, RH
中科院分区：
文献类型：
--
作者：
Oja, H;Randles, RH
针对单样本定位问题、多样本定位问题和向量对之间的独立性检验问题，描述了多变量非参数统计假设检验。这些方法是基于仿射不变的空间符号和空间秩向量。它们提供了单变量符号检验、符号秩检验、Wilcoxon秩和检验、Kruskal-Wallis检验以及Kendall和斯皮尔曼相关性检验的仿射不变多变量推广。虽然重点是假设检验，相关的仿射同变估计的某些参考资料。皮特曼渐近效率证明了这些方法的优良性能，特别是在重尾人口设置。此外，这些方法对于常见维度的数据很容易计算。
Multivariate nonparametric statistical tests of hypotheses are described for the one-sample location problem, the several-sample location problem and the problem of testing independence between pairs of vectors. These methods are based on affine-invariant spatial sign and spatial rank vectors. They provide affine-invariant multivariate generalizations of the univariate sign test, signed-rank test, Wilcoxon rank sum test, Kruskal-Wallis test, and the Kendall and Spearman correlation tests. While the emphasis is on tests of hypotheses, certain references to associated affine-equivariant estimators are included. Pitman asymptotic efficiencies demonstrate the excellent performance of these methods, particularly in heavy-tailed population settings. Moreover, these methods are easy to compute for data in common dimensions.