Bayesian Bandwidth Test and Selection for High-dimensional Banded Precision Matrices

TL;DR

This paper introduces a Bayesian method for estimating the bandwidth of high-dimensional banded precision matrices, establishing model selection consistency and outperforming existing methods in simulations.

Contribution

We propose a novel prior for Bayesian bandwidth estimation in high-dimensional precision matrices, proving consistency and deriving convergence rates.

Findings

Bayesian approach achieves model selection consistency.

Method outperforms existing frequentist and Bayesian techniques.

Effective two-sample bandwidth testing demonstrated.

Abstract

Assuming a banded structure is one of the common practice in the estimation of high-dimensional precision matrix. In this case, estimating the bandwidth of the precision matrix is a crucial initial step for subsequent analysis. Although there exist some consistent frequentist tests for the bandwidth parameter, bandwidth selection consistency for precision matrices has not been established in a Bayesian framework. In this paper, we propose a prior distribution tailored to the bandwidth estimation of high-dimensional precision matrices. The banded structure is imposed via the Cholesky factor from the modified Cholesky decomposition. We establish the strong model selection consistency for the bandwidth as well as the consistency of the Bayes factor. The convergence rates for Bayes factors under both the null and alternative hypotheses are derived which yield similar order of rates. As a by-product, we also proposed an estimation procedure for the Cholesky factors yielding an almost optimal order of convergence rates. Two-sample bandwidth test is also considered, and it turns out that our method is able to consistently detect the equality of bandwidths between two precision matrices. The simulation study confirms that our method in general outperforms the existing frequentist and Bayesian methods.