Semiparametric Modeling and Analysis for Longitudinal Network Data

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 th...

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Veröffentlicht in:arXiv.org 2024-07
Hauptverfasser: He, Yinqiu, Sun, Jiajin, Tian, Yuang, Ying, Zhiliang, Yang, Feng
Format: Artikel
Sprache:eng
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Zusammenfassung: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.
ISSN:2331-8422