Missing link survival analysis with applications to available pandemic data
It is shown how to overcome a new missing data problem in survival analysis. Iterative nonparametric techniques are utilized and the missing data information is both estimated and used for further estimation in each iterative step. Theory is developed and a good finite sample performance is illustra...
Gespeichert in:
| Hauptverfasser: | , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2022
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| In: |
Computational statistics & data analysis
Year: 2022, Jahrgang: 169, Pages: 1-18 |
| DOI: | 10.1016/j.csda.2021.107405 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.csda.2021.107405 Verlag, lizenzpflichtig, Volltext: https://www.sciencedirect.com/science/article/pii/S0167947321002395 
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| Verfasserangaben: | María Luz Gámiz, Enno Mammen, María Dolores Martínez-Miranda, Jens Perch Nielsen |
MARC
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| 520 | |a It is shown how to overcome a new missing data problem in survival analysis. Iterative nonparametric techniques are utilized and the missing data information is both estimated and used for further estimation in each iterative step. Theory is developed and a good finite sample performance is illustrated by simulations. The main motivation is an application to French data on the temporal development of the number of hospitalized Covid-19 patients. | ||
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