Where Do Two Populations of Persistence Diagrams Differ? Calibrated Local Inference at a Fixed Budget
Many two-sample tests for populations of persistence diagrams assess global differences without identifying the regions of the birth-death plane that contribute to them.
ProofPaper ↗
Key points
- We study simultaneous inference for local mean contrasts when the number of available diagrams is fixed.
- They are differences in expected weighted feature mass within $\ell\infty$ neighborhoods at several centers and radii.
- We estimate these contrasts using additive landmark responses.
- A Gaussian multiplier bootstrap calibrates simultaneous confidence intervals while allowing unequal group covariances.
Sources (1)
- [1]Where Do Two Populations of Persistence Diagrams Differ? Calibrated Local Inference at a Fixed BudgetarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 01:00 PM
Many two-sample tests for populations of persistence diagrams assess global differences without identifying the regions of the birth-death plane that contribute to them.
We study simultaneous inference for local mean contrasts when the number of available diagrams is fixed.
Extractive summary: sentences quoted from the sources.