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Activity Number: 545
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #321080 View Presentation
Title: Expandable Factor Analysis
Author(s): Sanvesh Srivastava* and Barbara E. Engelhardt and David Dunson
Companies: University of Iowa and Princeton and Duke University
Keywords: factor analysis ; high-dimensional loadings matrix ; non-concave variable selection ; sparsity ; generalized double Pareto ; EM-type algorithm
Abstract:

Bayesian sparse factor models have proven useful for characterizing dependence in multivariate data, but scaling computations to large number of samples and dimensions is problematic. We propose expandable factor analysis for scalable estimation in factor models. The method relies on a novel multiscale generalized double Pareto shrinkage prior that allows efficient estimation of low-rank and sparse loadings matrices through weighted L1-regularized regression. Efficient sampling and estimation algorithms are developed that accommodate uncertainty in the number of factors and that generalize to other settings. The methods are applied to simulated data and genomic studies.

Joint work with Barbara E. Engelhardt (Princeton University) and David B. Dunson (Duke University)


Authors who are presenting talks have a * after their name.

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