Adaptive Bayesian Spectral Analysis of High-dimensional Nonstationary Time Series.

ABSTRACT: This article introduces a nonparametric approach to spectral analysis of a high-dimensional multivariate nonstationary time series. The procedure is based on a novel frequency-domain factor model that provides a flexible yet parsimonious representation of spectral matrices from a large number of simultaneously observed time series. Real and imaginary parts of the factor loading matrices are modeled independently using a prior that is formulated from the tensor product of penalized splines and multiplicative gamma process shrinkage priors, allowing for infinitely many factors with loadings increasingly shrunk towards zero as the column index increases. Formulated in a fully Bayesian framework, the time series is adaptively partitioned into approximately stationary segments, where both the n

SUBMITTER: Li Z 

PROVIDER: S-EPMC9355453 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature