Permutation tests are an immensely popular statistical tool, used for testing hypotheses of independence between variables and other common inferential questions. When the number of observations is large, it is computationally infeasible to consider every possible permutation of the data, and it is typical to either take a random draw of permutations, or to restrict to a subgroup or subset of permutations. In this work, we extend beyond these possibilities to show how such tests can be run using any distribution over any subset of permutations, with all the previous options as a special case.
翻译:变异测试是一种非常流行的统计工具,用于测试变量和其他常见推断问题之间独立性的假设。 当观测数量大时,考虑数据的每一种可能的变异都是在计算上不可行的,通常要么随机抽取变异图,要么局限于分组或变异子组。在这项工作中,我们超越了这些可能性,以表明如何利用任何组合的分布进行这种测试,而以前的所有选项都是特殊情况。