Efficient empirical likelihood inference for recovery rate of COVID19 under double-censoring

Doubly censored data are very common in epidemiology studies. Ignoring censorship in the analysis may lead to biased parameter estimation. In this paper, we highlight that the publicly available COVID19 data may involve high percentage of double-censoring and point out the importance of dealing with...

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Detalles Bibliográficos
Autores principales: Hu, Jie, Liang, Wei, Dai, Hongsheng, Bao, Yanchun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V. 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9077865/
https://www.ncbi.nlm.nih.gov/pubmed/35573146
http://dx.doi.org/10.1016/j.jspi.2022.04.005

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9077865/
https://www.ncbi.nlm.nih.gov/pubmed/35573146
http://dx.doi.org/10.1016/j.jspi.2022.04.005

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