Approximating posteriors with high-dimensional nuisance parameters via integrated rotated Gaussian approximation.

Journal Article (Journal Article)

Posterior computation for high-dimensional data with many parameters can be challenging. This article focuses on a new method for approximating posterior distributions of a low- to moderate-dimensional parameter in the presence of a high-dimensional or otherwise computationally challenging nuisance parameter. The focus is on regression models and the key idea is to separate the likelihood into two components through a rotation. One component involves only the nuisance parameters, which can then be integrated out using a novel type of Gaussian approximation. We provide theory on approximation accuracy that holds for a broad class of forms of the nuisance component and priors. Applying our method to simulated and real data sets shows that it can outperform state-of-the-art posterior approximation approaches.

Full Text

Duke Authors

Cited Authors

  • VAN DEN Boom, W; Reeves, G; Dunson, DB

Published Date

  • June 2021

Published In

Volume / Issue

  • 108 / 2

Start / End Page

  • 269 - 282

PubMed ID

  • 35747172

Pubmed Central ID

  • PMC9216391

Electronic International Standard Serial Number (EISSN)

  • 1464-3510

International Standard Serial Number (ISSN)

  • 0006-3444

Digital Object Identifier (DOI)

  • 10.1093/biomet/asaa068


  • eng