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arXiv · 2308.12227

Semiparametric Modeling and Analysis for Longitudinal Network Data

Yinqiu He·Jiajin Sun·Yuang Tian·Zhiliang Ying·Yang Feng
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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.

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Yinqiu He, Jiajin Sun, Yuang Tian, Zhiliang Ying, Yang Feng. 2025-02-13. Semiparametric Modeling and Analysis for Longitudinal Network Data. https://arxiv.org/abs/2308.12227

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