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2019 Nonparametric inference for continuous-time event counting and link-based dynamic network models
Alexander Kreiß, Enno Mammen, Wolfgang Polonik
Electron. J. Statist. 13(2): 2764-2829 (2019). DOI: 10.1214/19-EJS1588

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 in an asymptotic framework where the number of nodes tends 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. "Nonparametric inference for continuous-time event counting and link-based dynamic network models." Electron. J. Statist. 13 (2) 2764 - 2829, 2019. https://doi.org/10.1214/19-EJS1588

Information

Received: 1 April 2018; Published: 2019
First available in Project Euclid: 21 August 2019

zbMATH: 07104730
MathSciNet: MR3995010
Digital Object Identifier: 10.1214/19-EJS1588

Subjects:
Primary: 62G05, 62G20
Secondary: 62N99

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Vol.13 • No. 2 • 2019
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