Multiscale Tests for Point Processes and Longitudinal Networks

TL;DR

This paper introduces a multiscale testing framework for point processes and longitudinal networks, enabling detection of local differences in intensity functions and community structures with theoretical guarantees and practical validation.

Contribution

It presents a novel multiscale discretization approach applicable to both two-sample and community detection problems, with proven minimax optimal power under certain conditions.

Findings

Method achieves minimax optimal power in two-sample testing.

Framework effectively identifies regions with differing intensities.

Demonstrated success on simulated and real datasets.

Abstract

We propose a new testing framework applicable to both the two-sample problem on point processes and the community detection problem on rectangular arrays of point processes, which we refer to as longitudinal networks; the latter problem is useful in situations where we observe interactions among a group of individuals over time. Our framework is based on a multiscale discretization scheme that consider not just the global null but also a collection of nulls local to small regions in the domain; in the two-sample problem, the local rejections tell us where the intensity functions differ and in the longitudinal network problem, the local rejections tell us when the community structure is most salient. We provide theoretical analysis for the two-sample problem and show that our method has minimax optimal power under a Holder continuity condition. We provide extensive simulation and real data analysis demonstrating the practicality of our proposed method.