Cargando…
Multiple imputation for discrete data: Evaluation of the joint latent normal model
Missing data are ubiquitous in clinical and social research, and multiple imputation (MI) is increasingly the methodology of choice for practitioners. Two principal strategies for imputation have been proposed in the literature: joint modelling multiple imputation (JM‐MI) and full conditional specif...
Autores principales: | Quartagno, Matteo, Carpenter, James R. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6618333/ https://www.ncbi.nlm.nih.gov/pubmed/30868652 http://dx.doi.org/10.1002/bimj.201800222 |
Ejemplares similares
-
An approximate Bayesian significance test for genomic evaluations
por: Wittenburg, Dörte, et al.
Publicado: (2018) -
A design criterion for symmetric model discrimination based on flexible nominal sets
por: Harman, Radoslav, et al.
Publicado: (2020) -
Effect size measures and their benchmark values for quantifying benefit or risk of medicinal products
por: Rahlfs, Volker, et al.
Publicado: (2019) -
Bayesian variable selection logistic regression with paired proteomic measurements
por: Kakourou, Alexia, et al.
Publicado: (2018) -
Substantive model compatible multilevel multiple imputation: A joint modeling approach
por: Quartagno, Matteo, et al.
Publicado: (2022)