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Expandable factor analysis

Author

Listed:
  • Sanvesh Srivastava
  • Barbara E. Engelhardt
  • David B. Dunson

Abstract

SummaryBayesian 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.

Suggested Citation

  • Sanvesh Srivastava & Barbara E. Engelhardt & David B. Dunson, 2017. "Expandable factor analysis," Biometrika, Biometrika Trust, vol. 104(3), pages 649-663.
    • Handle: RePEc:oup:biomet:v:104:y:2017:i:3:p:649-663.
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    File URL: http://hdl.handle.net/10.1093/biomet/asx030
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    Cited by:

    1. Pantelis Samartsidis & Shaun R. Seaman & Silvia Montagna & André Charlett & Matthew Hickman & Daniela De Angelis, 2020. "A Bayesian multivariate factor analysis model for evaluating an intervention by using observational time series data on multiple outcomes," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1437-1459, October.
    2. Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Foundations and Trends(R) in Econometrics, now publishers, vol. 11(4), pages 230-354, June.

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