论文标题
K-均值聚类的选择性推断
Selective inference for k-means clustering
论文作者
论文摘要
我们考虑测试通过K-均值聚类确定的观察结果群体之间的均值差异的问题。在这种情况下，经典假设检验导致I型错误率膨胀。为了克服这个问题，我们采用选择性推论方法。我们提出了一个有限的样本p值，该p值控制选择性I类型错误，以测试使用K-Means聚类获得的一对群集之间的差异，并证明可以有效地计算出来。我们将建议应用于仿真，并将手写数字数据和单细胞RNA-Sere-Ser-Se-Se-Se-Se-Se-Ser-Se-Se-Se-Se-Ser-Se-Ser-Se-se-seter数据应用于模拟中。
We consider the problem of testing for a difference in means between clusters of observations identified via k-means clustering. In this setting, classical hypothesis tests lead to an inflated Type I error rate. To overcome this problem, we take a selective inference approach. We propose a finite-sample p-value that controls the selective Type I error for a test of the difference in means between a pair of clusters obtained using k-means clustering, and show that it can be efficiently computed. We apply our proposal in simulation, and on hand-written digits data and single-cell RNA-sequencing data.
