Improving Multilevel Regression and Poststratification with Structured Priors

Bayesian Analysis

article

Andrew Gelman¹  Daniel Simpson²  Lauren Kennedy³  Yuxiang Gao² 

[1] Department of Statistics and Department of Political Science, Columbia University;Department of Statistical Sciences, University of Toronto;Columbia Population Research Center and Department of Statistics, Columbia University

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

DOI  :  10.1214/20-BA1223

学科分类：统计和概率

来源: Institute Of Mathematical Statistics

【 摘 要 】

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.

【 授权许可】

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