Improving multilevel regression and poststratification with structured priors

Yuxiang Gao, Lauren Kennedy, Daniel Simpson, Andrew Gelman

Research output: Contribution to journalArticleResearchpeer-review

Abstract

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.
Original languageEnglish
Number of pages26
JournalBayesian Analysis
DOIs
Publication statusAccepted/In press - 2020
Externally publishedYes

Keywords

  • multilevel regression and poststratification
  • non-representative data
  • bias reduction
  • small-area estimation
  • structured prior distributions
  • Stan
  • Integrated Nested Laplace Approximation (INLA)

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