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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...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Elsevier B.V.
2022
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| Materias: | |
| 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 |