Missing link survival analysis with applications to available pandemic data

• Verfasst von: Gámiz, María Luz [VerfasserIn]
• Verfasst von: Mammen, Enno [VerfasserIn]
• Verfasst von: Martinez Miranda, Maria Dolores [VerfasserIn]
• Verfasst von: Nielsen, Jens Perch [VerfasserIn]
• Titel: Missing link survival analysis with applications to available pandemic data
• Verf.angabe: María Luz Gámiz, Enno Mammen, María Dolores Martínez-Miranda, Jens Perch Nielsen
• Jahr: 2022
• Umfang: 18 S.
• Fussnoten: Available online 13 December 2021 ; Gesehen am 01.03.2022
• Titel Quelle: Enthalten in: Computational statistics & data analysis
• Ort Quelle: Amsterdam : Elsevier Science, 1983
• Jahr Quelle: 2022
• Band/Heft Quelle: 169(2022), Artikel-ID 107405, Seite 1-18
• DOI: doi:10.1016/j.csda.2021.107405
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Double one-sided cross-validation
• Sach-SW: Hazard
• Sach-SW: Local linear estimation
• Sach-SW: Missing data
• K10plus-PPN: 1794178074

Abstract

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.