Bayesian models for weighted data with missing values: a bootstrap approach

Journal article


Goldstein, Harvey, Carpenter, James and Kenward, Michael G.. (2018). Bayesian models for weighted data with missing values: a bootstrap approach. Journal of the Royal Statistical Society Series C: Applied Statistics. 67(4), pp. 1071 - 1081. https://doi.org/10.1111/rssc.12259
AuthorsGoldstein, Harvey, Carpenter, James and Kenward, Michael G.
Abstract

Many data sets, especially from surveys, are made available to users with weights. Where the derivation of such weights is known, this information can often be incorporated in the user’s substantive model (model of interest). When the derivation is unknown, the established procedure is to carry out a weighted analysis. However, with non-trivial proportions of missing data this is inefficient and may be biased when data are not missing at random. Bayesian approaches provide a natural approach for the imputation of missing data, but it is unclear how to handle the weights. We propose a weighted bootstrap Markov chain Monte Carlo algorithm for estimation and inference. A simulation study shows that it has good inferential properties. We illustrate its utility with an analysis of data from the Millennium Cohort Study

KeywordsMarkov chain Monte Carlo sampling; Millennium Cohort Study; missing data; weighted bootstrap
Year2018
JournalJournal of the Royal Statistical Society Series C: Applied Statistics
Journal citation67 (4), pp. 1071 - 1081
PublisherWiley Blackwell Publishing
ISSN0035-9254
Digital Object Identifier (DOI)https://doi.org/10.1111/rssc.12259
Scopus EID2-s2.0-85050252151
Page range1071 - 1081
Research GroupInstitute for Learning Sciences and Teacher Education (ILSTE)
Publisher's version
File Access Level
Controlled
Place of publicationUnited Kingdom
EditorsR. Boys and N. Stallard
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