Abstract:

Longitudinal network data have received growing attention due to demands from various applications. One common problem of interest is to recover the inherent time-invariant relations between subjects from time-varying interactions. We propose a semiparametric latent space modeling approach to the analysis of recurrent interaction events. The model consists of a static latent space component and a time-heterogeneous node-specific baseline component. We develop two novel semiparametric estimation methods for the latent space parameter, adjusting for the baseline nuisance component. The resulting estimation is achieved by a semiparametric one-step updating and a suitably penalized log-likelihood function. We develop novel semiparametric analysis techniques to establish oracle error bounds for the estimators.

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Topic: Colloquium -- Department of Statistics and Actuarial Science -- University of Iowa
Time: Mar 30, 2023 03:15 PM Central Time (US and Canada)

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Meeting ID: 919 6962 2615