Log In Sign Up

Bootstrapping Persistent Betti Numbers and Other Stabilizing Statistics

by   Benjamin Roycraft, et al.

The present contribution investigates multivariate bootstrap procedures for general stabilizing statistics, with specific application to topological data analysis. Existing limit theorems for topological statistics prove difficult to use in practice for the construction of confidence intervals, motivating the use of the bootstrap in this capacity. However, the standard nonparametric bootstrap does not provide for asymptotically valid confidence intervals in some situations. The smoothed bootstrap, instead, is shown to give consistent estimation where the standard bootstrap fails. The present work relates to other general results in the area of stabilizing statistics, including central limit theorems for functionals of Poisson and Binomial processes in the critical regime. Specific statistics considered include the persistent Betti numbers of Ĉech and Vietoris-Rips complexes over point sets in R^d, along with Euler characteristics, and minimum spanning trees. We further define a new type of B-bounded persistent homology, and investigate its fundamental properties. A simulation study is provided to assess the performance of the bootstrap for finite sample sizes. Data application is made to a cosmic web dataset from the Sloan Digital Sky Survey (SDSS).


page 1

page 2

page 3

page 4


Functional central limit theorems for persistent Betti numbers on cylindrical networks

We study functional central limit theorems (FCLTs) for persistent Betti ...

Consistency of full-sample bootstrap for estimating high-quantile, tail probability, and tail index

We show that the full-sample bootstrap is asymptotically valid for const...

On the asymptotic normality of persistent Betti numbers

Persistent Betti numbers are a major tool in persistent homology, a subf...

On approximation theorems for the Euler characteristic with applications to the bootstrap

We study approximation theorems for the Euler characteristic of the Viet...

Generalization error for decision problems

In this entry we review the generalization error for classification and ...

Parametric Bootstrap Confidence Intervals for the Multivariate Fay-Herriot Model

The multivariate Fay-Herriot model is quite effective in combining infor...

Nonparametric Bootstrap Inference for the Targeted Highly Adaptive LASSO Estimator

The Highly-Adaptive-LASSO Targeted Minimum Loss Estimator (HAL-TMLE) is ...