Flexible Bayesian Dynamic Modeling of Correlation and Covariance Matrices
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Open Access Publications from the University of California

Flexible Bayesian Dynamic Modeling of Correlation and Covariance Matrices

  • Author(s): Lan, S
  • Holbrook, A
  • Elias, GA
  • Fortin, NJ
  • Ombao, H
  • Shahbaba, B
  • et al.

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 correlation among multiple time series. To handle the intractability of the resulting posterior, we introduce the adaptive $\Delta$-Spherical Hamiltonian Monte Carlo. Using an example of normal-Inverse-Wishart problem, a simulated periodic process, and an analysis of local field potential activity data (collected from the hippocampus of rats performing a complex sequence memory task), we demonstrate the validity and effectiveness of our proposed framework for (dynamic) modeling covariance and correlation matrices.

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