On the Performance of Sequential Regression Multiple Imputation Methods with Non Normal Error Distributions

DOI10.1080/03610910802677191zbMath1160.62064OpenAlexW2126896423MaRDI QIDQ3625355

Yulei He, Trivellore E. Raghunathan

Publication date: 12 May 2009

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610910802677191

zbMATH Keywords

tablesresidualsextreme valuepredictive mean matchingmultivariate missing dataTukey's g- and h-distribution

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Parametric inference (62F99) Sequential statistical methods (62L99) Statistics of extreme values; tail inference (62G32)

Related Items (4)

Theoretical considerations when simulating data from the g‐and‐h family of distributions ⋮ A new multiple imputation method for bounded missing values ⋮ Multiple imputation using multivariateghtransformations ⋮ Multiple Imputation of Predictor Variables Using Generalized Additive Models

Cites Work

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• Evaluation of software for multiple imputation of semi-continuous data
• A comparison of imputation techniques for handling missing predictor values in a risk model with a binary outcome
• Plausibility of multivariate normality assumption when multiply imputing non-Gaussian continuous outcomes: a simulation assessment
• Multiple Imputation for Model Checking: Completed‐Data Plots with Missing and Latent Data
• Multiple Imputation of Missing Income Data in the National Health Interview Survey

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