Nonparametric inference for continuous-time event counting and link-based dynamic network models
Statistics Theory
2021-03-30 v5 Statistics Theory
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.
Cite
@article{arxiv.1705.03830,
title = {Nonparametric inference for continuous-time event counting and link-based dynamic network models},
author = {Alexander Kreiß and Enno Mammen and Wolfgang Polonik},
journal= {arXiv preprint arXiv:1705.03830},
year = {2021}
}