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New statistical method identifies localized differences in topological data analysis

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Researchers have developed a new statistical method for analyzing populations of persistence diagrams, which are used to identify topological features in data. This approach focuses on pinpointing specific regions within the birth-death plane that contribute to differences between two datasets, rather than just detecting overall dissimilarities. The method utilizes a Gaussian multiplier bootstrap to calibrate simultaneous confidence intervals and provides a way to visualize these localized differences with approximate family-wise error control. AI

RANK_REASON The cluster contains an academic paper detailing a new statistical methodology. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.LG →

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New statistical method identifies localized differences in topological data analysis

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The cluster contains an academic paper detailing a new statistical methodology. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Pramita Bagchi, Edward Bae, Atish Mitra, Alexander D. Silberman, \v{Z}iga Virk, Sushovan Majhi ·

    Where Do Two Populations of Persistence Diagrams Differ? Calibrated Local Inference at a Fixed Budget

    arXiv:2610.08292v1 Announce Type: cross Abstract: 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 whe…

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