Accurate Bayesian prediction in small area using samples with selection bias

Oregon Convention Center 

The small-area framework we consider includes instances where data is available from probability-based surveys as well as non-probability samples, and where the sample sizes from these two groups may be extremely imbalanced. We present a Bayesian algorithm and some alternatives for small-area prediction in this context. Several technical and algorithmic advancements related to the proposed technique lead to considerable broadening of the scope of using data with selection bias. We present theoretical advancements as well as results from numeric studies.