Bayesian Multilevel Latent Class Models for the Multiple Imputation of Nested Categorical Data

With this article, we propose using a Bayesian multilevel latent class (BMLC; or mixture) model for the multiple imputation of nested categorical data. Unlike recently developed methods that can only pick up associations between pairs of variables, the multilevel mixture model we propose is flexible...

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Detalles Bibliográficos
Autores principales: Vidotto, Davide, Vermunt, Jeroen K., van Deun, Katrijn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2018
Materias:
Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6187066/
https://www.ncbi.nlm.nih.gov/pubmed/30369783
http://dx.doi.org/10.3102/1076998618769871

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6187066/
https://www.ncbi.nlm.nih.gov/pubmed/30369783
http://dx.doi.org/10.3102/1076998618769871

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