A bootstrap approach to Bayesian models for weighted data


  • Author: Statistics Views
  • Date: 18 June 2018
  • Copyright: Image appears courtesy of Getty Images

Each week, we select a recently published article and provide free access. This week's is from the Journal of the Royal Statistical Society - Series C (Applied Statistics) and is available from Early View.

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

Harvey Goldstein, James Carpenter and Michael Kenward

Journal of the Royal Statistical Society - Series C (Applied Statistics), Early View, DOI: https://doi.org/10.1111/rssc.12259

The introduction is provided below:

thumbnail image: A bootstrap approach to Bayesian models for weighted data

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.

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