Yang Xi, Hannig Jan, Hoadley Katherine A, Carmichael Iain, Marron J S
Department of Statistics and Operations Research, University of North Carolina Chapel Hill, USA.
Department of Genetics, University of North Carolina Chapel Hill, USA.
J Comput Graph Stat. 2024;33(2):736-748. doi: 10.1080/10618600.2023.2276113. Epub 2023 Dec 26.
For measuring the strength of visually-observed subpopulation differences, the Population Difference Criterion is proposed to assess the statistical significance of visually observed subpopulation differences. It addresses the following challenges: in high-dimensional contexts, distributional models can be dubious; in high-signal contexts, conventional permutation tests give poor pairwise comparisons. We also make two other contributions: Based on a careful analysis we find that a balanced permutation approach is more powerful in high-signal contexts than conventional permutations. Another contribution is the quantification of uncertainty due to permutation variation via a bootstrap confidence interval. The practical usefulness of these ideas is illustrated in the comparison of subpopulations of modern cancer data.
为了衡量视觉观察到的亚群体差异的强度，提出了群体差异标准来评估视觉观察到的亚群体差异的统计显著性。它解决了以下挑战：在高维环境中，分布模型可能不可靠；在高信号环境中，传统的排列检验给出的成对比较效果不佳。我们还做出了另外两项贡献：经过仔细分析，我们发现平衡排列方法在高信号环境中比传统排列更有效。另一项贡献是通过自助置信区间量化由于排列变化导致的不确定性。这些想法在现代癌症数据亚群体的比较中得到了实际应用。
