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Modeling of persistent homology

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  • Sarit Agami
  • Robert J. Adler

Abstract

Topological Data Analysis (TDA) is a novel statistical technique, particularly powerful for the analysis of large and high dimensional data sets. Much of TDA is based on the tool of persistent homology, represented visually via persistence diagrams. In an earlier article we proposed a parametric representation for the probability distributions of persistence diagrams, and based on it provided a method for their replication. Since the typical situation for big data is that only one persistence diagram is available, these replications allow for conventional statistical inference, which, by its very nature, requires some form of replication. In the current paper we continue this analysis, and further develop its practical statistical methodology, by investigating a wider class of examples than treated previously.

Suggested Citation

  • Sarit Agami & Robert J. Adler, 2020. "Modeling of persistent homology," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 49(20), pages 4871-4888, October.
    • Handle: RePEc:taf:lstaxx:v:49:y:2020:i:20:p:4871-4888
    DOI: 10.1080/03610926.2019.1615091
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