Expandable factor analysis

Sanvesh Srivastava, Barbara Engelhardt Martin, David B. Dunson

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)649-663
Number of pages15
JournalBiometrika
Volume104
Issue number3
DOIs
StatePublished - Sep 1 2017

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Mathematics(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Keywords

  • Expectation-maximization algorithm
  • Factor analysis
  • Shrinkage prior
  • Sparsity
  • Variable selection

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