Improving multilevel regression and poststratification with structured priors

DOI10.1214/20-ba1223arXiv1908.06716OpenAlexW3099557721MaRDI QIDQ6120423

Andrew Gelman, Daniel P. Simpson, Yuxiang Gao, Lauren Kennedy

Publication date: 20 February 2024

Published in: Bayesian Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1908.06716

zbMATH Keywords

bias reductionsmall-area estimationintegrated nested Laplace approximation (INLA)Stanmultilevel regression and poststratificationnon-representative datastructured prior distributions

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Modeling racial/ethnic differences in COVID-19 incidence with covariates subject to nonrandom missingness

Cites Work

• An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
• New important developments in small area estimation
• Introduction to the design and analysis of complex survey data
• Inference for nonprobability samples
• Penalising model component complexity: a principled, practical approach to constructing priors
• Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
• Post-Stratification: A Modeler's Perspective
• Gaussian Markov Random Fields
• Advanced Lectures on Machine Learning

This page was built for publication: Improving multilevel regression and poststratification with structured priors