Iterative Multiple Imputation: A Framework to Determine the Number of Imputed Datasets

DOI10.1080/00031305.2018.1543615OpenAlexW2903929244WikidataQ128760170 ScholiaQ128760170MaRDI QIDQ5869267

Vahid Nassiri, João Barbosa-Breda, Geert Verbeke, Geert Molenberghs

Publication date: 28 September 2022

Published in: The American Statistician (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00031305.2018.1543615

zbMATH Keywords

iterative procedureincomplete datacentral limit theoremmissing data

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Estimating longitudinal change in latent variable means: a comparison of non-negative matrix factorization and other item non-response methods

Uses Software

• Stata
• mvis

Cites Work

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• Stein's method and a quantitative Lindeberg CLT for the Fourier transforms of random vectors
• On the rate of convergence in the multivariate CLT
• Fully conditional specification in multivariate imputation
• Nonparametric estimates of standard error: The jackknife, the bootstrap and other methods
• Multiple Imputation for Interval Estimation From Simple Random Samples With Ignorable Nonresponse
• Generating missing values for simulation purposes: a multivariate amputation procedure
• Fast, closed-form, and efficient estimators for hierarchical models with AR(1) covariance and unequal cluster sizes
• Multiple imputation of discrete and continuous data by fully conditional specification
• Contributions to the Theory of Statistical Estimation and Testing Hypotheses

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