Selective Inference for Clustering Under Unknown Covariance
Unsupervised clustering is widely used to discover underlying group structure, after which researchers often seek to validate these groups via post-hoc hypothesis testing on the population means. However, using the same data both to form clusters and to test for a difference in means inflates the Type I error rate. Recent work in selective inference has proposed solutions when the underlying data are drawn from a multivariate Gaussian distribution with a known covariance matrix or an unknown isotropic covariance matrix. We propose a new selective test for the difference in means between two clusters that provably controls the Type I error rate under an arbitrary unknown covariance matrix....
| Authors | Yiqun T. Chen, Lucy L. Gao |
|---|---|
| Primary category | stat.ME |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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