Flexible Bayesian Dynamic Modeling of Correlation and Covariance Matrices.

ABSTRACT: Modeling correlation (and covariance) matrices can be challenging due to the positive-definiteness constraint and potential high-dimensionality. Our approach is to decompose the covariance matrix into the correlation and variance matrices and propose a novel Bayesian framework based on modeling the correlations as products of unit vectors. By specifying a wide range of distributions on a sphere (e.g. the squared-Dirichlet distribution), the proposed approach induces flexible prior distributions for covariance matrices (that go beyond the commonly used inverse-Wishart prior). For modeling real-life spatio-temporal processes with complex dependence structures, we extend our method to dynamic cases and introduce unit-vector Gaussian process priors in order to capture the evolution of correlat

SUBMITTER: Lan S 

PROVIDER: S-EPMC8048134 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature