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

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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...

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Main Authors: Kreiß, Alexander (Author) , Mammen, Enno (Author) , Polonik, Wolfgang (Author)
Format: Article (Journal)
Language:English
Published: 21 August 2019
In: Electronic journal of statistics
Year: 2019, Volume: 13, Issue: 2, Pages: 2764-2829
ISSN:1935-7524
DOI:10.1214/19-EJS1588
Online Access:Verlag, Volltext: https://doi.org/10.1214/19-EJS1588
Verlag, Volltext: https://projecteuclid.org/euclid.ejs/1566353062
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Author Notes:Alexander Kreiß, Enno Mammen, Wolfgang Polonik
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Nonparametric inference for continuous-time event counting and link-based dynamic network models by Kreiß, Alexander (Author) , Mammen, Enno (Author) , Polonik, Wolfgang (Author) ,


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