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August 2020 Bayesian analysis of the covariance matrix of a multivariate normal distribution with a new class of priors
James O. Berger, Dongchu Sun, Chengyuan Song
Ann. Statist. 48(4): 2381-2403 (August 2020). DOI: 10.1214/19-AOS1891

Abstract

Bayesian analysis for the covariance matrix of a multivariate normal distribution has received a lot of attention in the last two decades. In this paper, we propose a new class of priors for the covariance matrix, including both inverse Wishart and reference priors as special cases. The main motivation for the new class is to have available priors—both subjective and objective—that do not “force eigenvalues apart,” which is a criticism of inverse Wishart and Jeffreys priors. Extensive comparison of these “shrinkage priors” with inverse Wishart and Jeffreys priors is undertaken, with the new priors seeming to have considerably better performance. A number of curious facts about the new priors are also observed, such as that the posterior distribution will be proper with just three vector observations from the multivariate normal distribution—regardless of the dimension of the covariance matrix—and that useful inference about features of the covariance matrix can be possible. Finally, a new MCMC algorithm is developed for this class of priors and is shown to be computationally effective for matrices of up to 100 dimensions.

Citation

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James O. Berger. Dongchu Sun. Chengyuan Song. "Bayesian analysis of the covariance matrix of a multivariate normal distribution with a new class of priors." Ann. Statist. 48 (4) 2381 - 2403, August 2020. https://doi.org/10.1214/19-AOS1891

Information

Received: 1 August 2018; Revised: 1 June 2019; Published: August 2020
First available in Project Euclid: 14 August 2020

MathSciNet: MR4134799
Digital Object Identifier: 10.1214/19-AOS1891

Subjects:
Primary: 62F15
Secondary: 62C10, 62H10, 62H86

Rights: Copyright © 2020 Institute of Mathematical Statistics

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Vol.48 • No. 4 • August 2020
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