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.

References in corpus (4)

• Dynamic stochastic blockmodels for time-evolving social networks
• Latent Space Models for Dynamic Networks
• Nonparametric Bayes dynamic modeling of relational data
• Nonparametric Multi-group Membership Model for Dynamic Networks

Cited by in corpus (3)

• Separable and Semiparametric Network-based Counting Processes applied to the International Combat Aircraft Trades
• Testing For Global Covariate Effects in Dynamic Interaction Event Networks
• Correlation bounds, mixing and m-dependence under random time-varying network distances with an application to Cox-Processes