Latent class based multiple imputation approach for missing categorical data

DOI10.1016/j.jspi.2010.04.020zbMath1204.62125OpenAlexW2053898507WikidataQ90569304 ScholiaQ90569304MaRDI QIDQ989249

Stacia M. DeSantis, Mulugeta Gebregziabher

Publication date: 19 August 2010

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc6290917

zbMATH Keywords

biasmissing datamultiple imputationlatent classcase-control data

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12)

Related Items

Divisive latent class modeling as a density estimation method for categorical data ⋮ Prediction and Inference With Missing Data in Patient Alert Systems ⋮ Incorporating marginal prior information in latent class models ⋮ Nonparametric estimation of a latent variable model ⋮ Multiple imputation: a review of practical and theoretical findings ⋮ Handling missing data in matched case‐control studies using multiple imputation

Uses Software

• SAS
• LCA

Cites Work

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