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June 2014 Posterior contraction in sparse Bayesian factor models for massive covariance matrices
Debdeep Pati, Anirban Bhattacharya, Natesh S. Pillai, David Dunson
Ann. Statist. 42(3): 1102-1130 (June 2014). DOI: 10.1214/14-AOS1215

Abstract

Sparse Bayesian factor models are routinely implemented for parsimonious dependence modeling and dimensionality reduction in high-dimensional applications. We provide theoretical understanding of such Bayesian procedures in terms of posterior convergence rates in inferring high-dimensional covariance matrices where the dimension can be larger than the sample size. Under relevant sparsity assumptions on the true covariance matrix, we show that commonly-used point mass mixture priors on the factor loadings lead to consistent estimation in the operator norm even when $p\gg n$. One of our major contributions is to develop a new class of continuous shrinkage priors and provide insights into their concentration around sparse vectors. Using such priors for the factor loadings, we obtain similar rate of convergence as obtained with point mass mixture priors. To obtain the convergence rates, we construct test functions to separate points in the space of high-dimensional covariance matrices using insights from random matrix theory; the tools developed may be of independent interest. We also derive minimax rates and show that the Bayesian posterior rates of convergence coincide with the minimax rates upto a $\sqrt{\log n}$ term.

Citation

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Debdeep Pati. Anirban Bhattacharya. Natesh S. Pillai. David Dunson. "Posterior contraction in sparse Bayesian factor models for massive covariance matrices." Ann. Statist. 42 (3) 1102 - 1130, June 2014. https://doi.org/10.1214/14-AOS1215

Information

Published: June 2014
First available in Project Euclid: 20 May 2014

zbMATH: 1305.62124
MathSciNet: MR3210997
Digital Object Identifier: 10.1214/14-AOS1215

Subjects:
Primary: 62G05, 62G20

Rights: Copyright © 2014 Institute of Mathematical Statistics

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Vol.42 • No. 3 • June 2014
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