Expandable factor analysis.

Journal Article

Bayesian sparse factor models have proven useful for characterizing dependence in multivariate data, but scaling computation to large numbers of samples and dimensions is problematic. We propose expandable factor analysis for scalable inference in factor models when the number of factors is unknown. The method relies on a continuous shrinkage prior for efficient maximum a posteriori estimation of a low-rank and sparse loadings matrix. The structure of the prior leads to an estimation algorithm that accommodates uncertainty in the number of factors. We propose an information criterion to select the hyperparameters of the prior. Expandable factor analysis has better false discovery rates and true positive rates than its competitors across diverse simulation settings. We apply the proposed approach to a gene expression study of ageing in mice, demonstrating superior results relative to four competing methods.

Full Text

Duke Authors

Cited Authors

  • Srivastava, S; Engelhardt, BE; Dunson, DB

Published Date

  • September 2017

Published In

Volume / Issue

  • 104 / 3

Start / End Page

  • 649 - 663

PubMed ID

  • 29430037

Electronic International Standard Serial Number (EISSN)

  • 1464-3510

International Standard Serial Number (ISSN)

  • 0006-3444

Digital Object Identifier (DOI)

  • 10.1093/biomet/asx030

Language

  • eng