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arXiv · 1705.03830

Nonparametric inference for continuous-time event counting and link-based dynamic network models

Alexander Kreiß·Enno Mammen·Wolfgang Polonik
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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.

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Alexander Kreiß, Enno Mammen, Wolfgang Polonik. 2019-05-28. Nonparametric inference for continuous-time event counting and link-based dynamic network models. https://doi.org/10.1214/19-ejs1588

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