Multiply imputing missing values in data sets with mixed measurement scales using a sequence of generalised linear models

DOI10.1016/j.csda.2015.08.004zbMath1468.62113OpenAlexW1531289110MaRDI QIDQ1659480

Robin Mitra, Min Cherng Lee

Publication date: 15 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://eprints.soton.ac.uk/361994/1/leemitras3riworkingpaper.pdf

zbMATH Keywords

missing datamultiple imputationdata augmentationlatent variable

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Sampling theory, sample surveys (62D05)

Related Items (2)

Multiple imputation: a review of practical and theoretical findings ⋮ Statistical Disclosure Control for Data Privacy Using Sequence of Generalised Linear Models

Uses Software

• MICE
• BayesDA

Cites Work

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• Bayesian Analysis of Binary and Polychotomous Response Data
• On the stationary distribution of iterative imputations
• Multiple Imputation for Model Checking: Completed‐Data Plots with Missing and Latent Data
• Missing-Data Methods for Generalized Linear Models

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