2019/08/31 by Yuxiang Gao, Lauren Kennedy, Daniel Simpson +1 · 1 citation
Mathematics · #stat.ME
paper · pdf · doi:10.1214/20-ba1223
published as Bayesian Analysis, Advance publication (2020), 26 pages · Minor revision. Added plots showing share of simulations where structured priors outperformed baseline priors in MRP
arxiv created 2020/05/23 · arxiv updated 2020/07/17
A central theme in the field of survey statistics is estimating population-level quantities through data coming from potentially non-representative samples of the population. Multilevel Regression and Poststratification (MRP), a model-based approach, is gaining traction against the traditional weighted approach for survey estimates. MRP estimates are susceptible to bias if there is an underlying structure that the methodology does not capture. This work aims to provide a new framework for specifying structured prior distributions that lead to bias reduction in MRP estimates. We use simulation studies to explore the benefit of these prior distributions and demonstrate their efficacy on non-representative US survey data. We show that structured prior distributions offer absolute bias reduction and variance reduction for posterior MRP estimates in a large variety of data regimes.