@article{biscio_accumulated_2019,
abstract = {We start with a simple introduction to topological data analysis where the most popular tool is called a persistence diagram. Briefly, a persistence diagram is a multiset of points in the plane describing the persistence of topological features of a compact set when a scale parameter varies. Since statistical methods are difficult to apply directly on persistence diagrams, various alternative functional summary statistics have been suggested, but either they do not contain the full information of the persistence diagram or they are two-dimensional functions. We suggest a new functional summary statistic that is one-dimensional and hence easier to handle, and which under mild conditions contains the full information of the persistence diagram. Its usefulness is illustrated in statistical settings concerned with point clouds and brain artery trees. The supplementary materials include additional methods and examples, technical details, and the R code used for all examples. © 2019, © 2019 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.},
author = {Biscio, C.A.N. and Møller, J.},
date = {2019},
doi = {10.1080/10618600.2019.1573686},
issn = {1061-8600},
journaltitle = {Journal of Computational and Graphical Statistics},
keywords = {1 - Blood vessels, 1 - Circulatory system, 1 - Medicine, 2 - Clustering, 2 - Persistent homology, 3 - Point cloud},
number = {3},
pages = {671--681},
title = {The Accumulated Persistence Function, a New Useful Functional Summary Statistic for Topological Data Analysis, With a View to Brain Artery Trees and Spatial Point Process Applications},
volume = {28}
}