Bayesian hierarchical weighting adjustment and survey inference

Understand

We combine Bayesian prediction and weighted inference as a unified approach to survey inference.

• The general principles of Bayesian analysis imply that models for survey outcomes should be conditional on all variables that affect the probability of inclusion.
• We incorporate the weighting variables under the framework of multilevel regression and poststratification, as a byproduct generating model-based weights after smoothing.
• We investigate deep interactions and introduce structured prior distributions for smoothing and stability of estimates.