Title: Nonparametric inference for continuous-time event counting and link-based dynamic network models,
Abstract: A flexible approach for modeling both dynamic event counting and dynamic
link-based networks based on counting processes is proposed, and estimation in
these models is studied. We consider nonparametric likelihood based estimation
of parameter functions via kernel smoothing. The asymptotic behavior of these
estimators is rigorously analyzed by allowing the number of nodes to tend to
infinity. The finite sample performance of the estimators is illustrated
through an empirical analysis of bike share data.