Using mixtures of \(t\) densities to make inferences in the presence of missing data with a small number of multiply imputed data sets

DOI10.1016/j.csda.2015.05.009zbMath1468.62167OpenAlexW824864210MaRDI QIDQ1663148

Y. Aharonov

Publication date: 21 August 2018

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

Full work available at URL: https://doi.org/10.1016/j.csda.2015.05.009

zbMATH Keywords

Monte CarloBayesian statisticsmissing datamultiple imputationmixture distributiondisclosure avoidance

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Sampling theory, sample surveys (62D05) Missing data (62D10)

Related Items (1)

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

Uses Software

• R
• WinBUGS
• MICE
• mi
• BayesDA
• BRENT

Cites Work

• The Multiple Adaptations of Multiple Imputation
• A comparison of posterior simulation and inference by combining rules for multiple imputation
• Multiple Imputation After 18+ Years
• On the Bias of the Multiple-Imputation Variance Estimator in Survey Sampling
• Multiple Imputation for Interval Estimation From Simple Random Samples With Ignorable Nonresponse
• Proper and Improper Multiple Imputation
• Miscellanea. Small-sample degrees of freedom with multiple imputation
• Inference for imputation estimators
• Integration of Multimodal Functions by Monte Carlo Importance Sampling
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