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Auteurs principaux:	He, Yinqiu, Sun, Jiajin, Tian, Yuang, Ying, Zhiliang, Feng, Yang
Format:	Preprint
Publié:	2023
Sujets:	Statistics Theory Methodology 62H12, 05C82, 91D30, 62F12
Accès en ligne:	https://arxiv.org/abs/2308.12227
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author	He, Yinqiu Sun, Jiajin Tian, Yuang Ying, Zhiliang Feng, Yang
author_facet	He, Yinqiu Sun, Jiajin Tian, Yuang Ying, Zhiliang Feng, Yang
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2308_12227
institution	arXiv
publishDate	2023
record_format	arxiv
spellingShingle	Semiparametric Modeling and Analysis for Longitudinal Network Data He, Yinqiu Sun, Jiajin Tian, Yuang Ying, Zhiliang Feng, Yang Statistics Theory Methodology 62H12, 05C82, 91D30, 62F12 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.
title	Semiparametric Modeling and Analysis for Longitudinal Network Data
topic	Statistics Theory Methodology 62H12, 05C82, 91D30, 62F12
url	https://arxiv.org/abs/2308.12227

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