The Annals of Statistics

Confidence sets for persistence diagrams

Brittany Terese Fasy, Fabrizio Lecci, Alessandro Rinaldo, Larry Wasserman, Sivaraman Balakrishnan, and Aarti Singh

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Persistent homology is a method for probing topological properties of point clouds and functions. The method involves tracking the birth and death of topological features (2000) as one varies a tuning parameter. Features with short lifetimes are informally considered to be “topological noise,” and those with a long lifetime are considered to be “topological signal.” In this paper, we bring some statistical ideas to persistent homology. In particular, we derive confidence sets that allow us to separate topological signal from topological noise.

Article information

Ann. Statist., Volume 42, Number 6 (2014), 2301-2339.

First available in Project Euclid: 20 October 2014

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62G05: Estimation 62G20: Asymptotic properties
Secondary: 62H12: Estimation

Persistent homology topology density estimation


Fasy, Brittany Terese; Lecci, Fabrizio; Rinaldo, Alessandro; Wasserman, Larry; Balakrishnan, Sivaraman; Singh, Aarti. Confidence sets for persistence diagrams. Ann. Statist. 42 (2014), no. 6, 2301--2339. doi:10.1214/14-AOS1252.

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Supplemental materials

  • Supplementary material: Supplement to “Confidence sets for persistence diagrams”. In the supplementary material we give a brief introduction to persistence homology and provide additional details about homology, simplicial complexes and stability of persistence diagrams.