Semiparametric Modeling and Analysis for Longitudinal Network Data
Statistics Theory
2025-02-14 v3 Methodology
Statistics Theory
Abstract
We introduce a semiparametric latent space model for analyzing longitudinal network data. The model consists of a static latent space component and a time-varying node-specific baseline component. We develop a semiparametric efficient score equation for the latent space parameter by adjusting for the baseline nuisance component. Estimation is accomplished through a one-step update estimator and an appropriately penalized maximum likelihood estimator. We derive oracle error bounds for the two estimators and address identifiability concerns from a quotient manifold perspective. Our approach is demonstrated using the New York Citi Bike Dataset.
Cite
@article{arxiv.2308.12227,
title = {Semiparametric Modeling and Analysis for Longitudinal Network Data},
author = {Yinqiu He and Jiajin Sun and Yuang Tian and Zhiliang Ying and Yang Feng},
journal= {arXiv preprint arXiv:2308.12227},
year = {2025}
}