Goodness-of-fit via Count Statistics in Dense Random Simplicial Complexes

TL;DR

This paper introduces a multi-parameter random simplicial complex model, establishes statistical properties including CLTs and asymptotic behavior of estimators, and develops a goodness-of-fit test for the model.

Contribution

It provides a unified probabilistic framework for various random complexes and introduces new statistical tools for model validation.

Findings

Multivariate CLTs with bounds for subcomplex counts

Asymptotic properties of MLE estimators

Effective goodness-of-fit testing method

Abstract

A key object of study in stochastic topology is a random simplicial complex. In this work we study a multi-parameter random simplicial complex model, where the probability of including a $k$-simplex, given the lower dimensional structure, is fixed. This leads to a conditionally independent probabilistic structure. This model includes the Erd\H{o}s-R\'enyi random graph, the random clique complex as well as the Linial-Meshulam complex as special cases. The model is studied from both probabilistic and statistical points of view. We prove multivariate central limit theorems with bounds and known limiting covariance structure for the subcomplex counts and the number of critical simplices under a lexicographical acyclic partial matching. We use the CLTs to develop a goodness-of-fit test for this random model and evaluate its empirical performance. In order for the test to be applicable in practice, we also prove that the MLE estimators are asymptotically unbiased, consistent, uncorrelated and normally distributed.