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...
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| Main Authors: | , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
2022
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| In: |
Computational statistics & data analysis
Year: 2022, Volume: 169, Pages: 1-18 |
| DOI: | 10.1016/j.csda.2021.107405 |
| Online Access: | 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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| Author Notes: | María Luz Gámiz, Enno Mammen, María Dolores Martínez-Miranda, Jens Perch Nielsen |
| Summary: | 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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| Item Description: | Available online 13 December 2021 Gesehen am 01.03.2022 |
| Physical Description: | Online Resource |
| DOI: | 10.1016/j.csda.2021.107405 |