Nonparametric inference for continuous-time event counting and link-based dynamic network models
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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| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
21 August 2019
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| In: |
Electronic journal of statistics
Year: 2019, Jahrgang: 13, Heft: 2, Pages: 2764-2829 |
| ISSN: | 1935-7524 |
| DOI: | 10.1214/19-EJS1588 |
| Online-Zugang: | Verlag, Volltext: https://doi.org/10.1214/19-EJS1588 Verlag, Volltext: https://projecteuclid.org/euclid.ejs/1566353062 
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| Verfasserangaben: | Alexander Kreiß, Enno Mammen, Wolfgang Polonik |
MARC
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| 520 | |a 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. | ||
| 650 | 4 | |a Asymptotic normality | |
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| 650 | 4 | |a local likelihood estimation | |
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