Imputation for statistical inference with coarse data

DOI10.1002/cjs.11142zbMath1349.62024OpenAlexW2027581521MaRDI QIDQ2856561

Minki Hong, Jae Kwang Kim

Publication date: 29 October 2013

Published in: Canadian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/cjs.11142

zbMATH Keywords

EM algorithmgrouped datafractional imputationmeasurement error models

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Sampling theory, sample surveys (62D05) Nonparametric statistical resampling methods (62G09)

Related Items (4)

A stochastic variant of the EM algorithm to fit mixed (discrete and continuous) longitudinal data with nonignorable missingness ⋮ Calibrated imputation of numerical data under linear edit restrictions ⋮ Analysis of inaccurate data with mixture measurement error models ⋮ Fractional imputation in survey sampling: a comparative review

Uses Software

• AS 139

Cites Work

• Parametric fractional imputation for missing data analysis
• Ignorability and coarse data
• Bias and efficiency loss due to categorizing an explanatory variable.
• Inference and missing data
• Large-sample theory for parametric multiple imputation procedures
• Maximizing Generalized Linear Mixed Model Likelihoods With an Automated Monte Carlo EM Algorithm
• Expected Estimating Equations to Accommodate Covariate Measurement Error
• Measurement Error in Nonlinear Models
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