Adaptive Bayesian Time-Frequency Analysis of Multivariate Time Series.

ABSTRACT: This article introduces a nonparametric approach to multivariate time-varying power spectrum analysis. The procedure adaptively partitions a time series into an unknown number of approximately stationary segments, where some spectral components may remain unchanged across segments, allowing components to evolve differently over time. Local spectra within segments are fit through Whittle likelihood based penalized spline models of modified Cholesky components, which provide flexible nonparametric estimates that preserve positive definite structures of spectral matrices. The approach is formulated in a Bayesian framework, in which the number and location of partitions are random, and relies on reversible jump Markov chain and Hamiltonian Monte Carlo methods that can adapt to the unknown numb

SUBMITTER: Li Z 

PROVIDER: S-EPMC6541451 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature