Nonstandard conditionally specified models for nonignorable missing data

Data analyses typically rely upon assumptions about the missingness mechanisms that lead to observed versus missing data, assumptions that are typically unassessable. We explore an approach where the joint distribution of observed data and missing data are specified in a nonstandard way. In this for...

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Detalles Bibliográficos
Autores principales: Franks, Alexander M., Airoldi, Edoardo M., Rubin, Donald B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2020
Materias:
Physical Sciences
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7430986/
https://www.ncbi.nlm.nih.gov/pubmed/32723822
http://dx.doi.org/10.1073/pnas.1815563117

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