Using an approximate Bayesian bootstrap to multiply impute nonignorable missing data

DOI10.1016/J.CSDA.2008.07.042zbMath1231.62037OpenAlexW2052502784WikidataQ33519029 ScholiaQ33519029MaRDI QIDQ961124

Thomas R. Belin, Juned Siddique

Publication date: 30 March 2010

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

Full work available at URL: http://europepmc.org/articles/pmc2678725

Mathematics Subject Classification ID

Bayesian inference (62F15) Bootstrap, jackknife and other resampling methods (62F40) Nonparametric statistical resampling methods (62G09)

Related Items (8)

BAGEL: a non-ignorable missing value estimation method for mixed attribute datasets ⋮ A hot deck imputation procedure for multiply imputing nonignorable missing data: the proxy pattern-mixture hot deck ⋮ Variable selection and prediction with incomplete high-dimensional data ⋮ Impact of the non-distinctness and non-ignorability on the inference by multiple imputation in multivariate multilevel data: a simulation assessment ⋮ Addressing missing data mechanism uncertainty using multiple-model multiple imputation: application to a longitudinal clinical trial ⋮ An imputation method for categorical variables with application to nonlinear principal component analysis ⋮ Bayesian bootstraps for massive data ⋮ Multiple imputation for the analysis of incomplete compound variables

Cites Work

• Unnamed Item
• Unnamed Item
• Partially parametric techniques for multiple imputation
• On the performance of bias-reduction techniques for variance estimation in approximate Bayesian bootstrap imputation
• Missing Data in Longitudinal Studies
• Inference and missing data
• Multiple Imputation for Interval Estimation From Simple Random Samples With Ignorable Nonresponse
• Observational studies.

This page was built for publication: Using an approximate Bayesian bootstrap to multiply impute nonignorable missing data