# Nonparametric inference for continuous-time event counting and link-based dynamic network models

## TL;DR

This paper introduces a nonparametric likelihood-based method for modeling and estimating dynamic event counting and link-based networks using kernel smoothing, with theoretical analysis and empirical validation.

## Contribution

It develops a nonparametric estimation framework for continuous-time network models and analyzes their asymptotic properties as the network size grows.

## Key findings

- Asymptotic properties of estimators are rigorously established.
- Finite sample performance demonstrated on bike share data.
- Method effectively captures dynamic network behaviors.

## Abstract

A flexible approach for modeling both dynamic event counting and dynamic link-based networks based on counting processes is proposed, and estimation in these models is studied. We consider nonparametric likelihood based estimation of parameter functions via kernel smoothing. The asymptotic behavior of these estimators is rigorously analyzed by allowing the number of nodes to tend to infinity. The finite sample performance of the estimators is illustrated through an empirical analysis of bike share data.