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Expandable factor analysis.

Publication ,  Journal Article
Srivastava, S; Engelhardt, BE; Dunson, DB
Published in: Biometrika
September 2017

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.

Duke Scholars

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Published In

Biometrika

DOI

EISSN

1464-3510

ISSN

0006-3444

Publication Date

September 2017

Volume

104

Issue

3

Start / End Page

649 / 663

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1403 Econometrics
  • 0104 Statistics
  • 0103 Numerical and Computational Mathematics
 

Citation

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Srivastava, S., Engelhardt, B. E., & Dunson, D. B. (2017). Expandable factor analysis. Biometrika, 104(3), 649–663. https://doi.org/10.1093/biomet/asx030
Srivastava, Sanvesh, Barbara E. Engelhardt, and David B. Dunson. “Expandable factor analysis.Biometrika 104, no. 3 (September 2017): 649–63. https://doi.org/10.1093/biomet/asx030.
Srivastava S, Engelhardt BE, Dunson DB. Expandable factor analysis. Biometrika. 2017 Sep;104(3):649–63.
Srivastava, Sanvesh, et al. “Expandable factor analysis.Biometrika, vol. 104, no. 3, Sept. 2017, pp. 649–63. Epmc, doi:10.1093/biomet/asx030.
Srivastava S, Engelhardt BE, Dunson DB. Expandable factor analysis. Biometrika. 2017 Sep;104(3):649–663.
Journal cover image

Published In

Biometrika

DOI

EISSN

1464-3510

ISSN

0006-3444

Publication Date

September 2017

Volume

104

Issue

3

Start / End Page

649 / 663

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1403 Econometrics
  • 0104 Statistics
  • 0103 Numerical and Computational Mathematics