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
Authors | Goldstein, Harvey, Carpenter, James and Kenward, Michael G. |
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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 |
Keywords | Markov chain Monte Carlo sampling; Millennium Cohort Study; missing data; weighted bootstrap |
Year | 2018 |
Journal | Journal of the Royal Statistical Society Series C: Applied Statistics |
Journal citation | 67 (4), pp. 1071 - 1081 |
Publisher | Wiley Blackwell Publishing |
ISSN | 0035-9254 |
Digital Object Identifier (DOI) | https://doi.org/10.1111/rssc.12259 |
Scopus EID | 2-s2.0-85050252151 |
Page range | 1071 - 1081 |
Research Group | Institute for Learning Sciences and Teacher Education (ILSTE) |
Publisher's version | File Access Level Controlled |
Place of publication | United Kingdom |
Editors | R. Boys and N. Stallard |
https://acuresearchbank.acu.edu.au/item/863x7/bayesian-models-for-weighted-data-with-missing-values-a-bootstrap-approach
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