TY  - JOUR
ID  - discovery10056606
AV  - public
KW  - Markov chain Monte Carlo sampling
KW  -  Millennium Cohort Study
KW  -  Missing data
KW  -  Weighted bootstrap
EP  - 1081
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
JF  - Journal of the Royal Statistical Society: Series C (Applied Statistics)
IS  - 4
SN  - 1467-9876
PB  - WILEY
VL  - 67
SP  - 1071
N2  - 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.
A1  - Goldstein, H
A1  - Carpenter, J
A1  - Kenward, MG
Y1  - 2018/08/01/
UR  - http://doi.org/10.1111/rssc.12259
TI  - Bayesian models for weighted data with missing values: a bootstrap approach
ER  -