Adaptive Bayesian Time–Frequency Analysis of Multivariate Time Series

DOI10.1080/01621459.2017.1415908zbMath1478.62262arXiv1706.05661OpenAlexW2785189499WikidataQ92477393 ScholiaQ92477393MaRDI QIDQ5229927

Robert T. Krafty, Zeda Li

Publication date: 19 August 2019

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1706.05661

zbMATH Keywords

spectral analysisreversible jump Markov chain Monte Carlopenalized splinesmodified Cholesky decompositionlocally stationary processnonstationary multivariate time series

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Bayesian inference (62F15) Inference from stochastic processes and spectral analysis (62M15)

Related Items (10)

Nonparametric Bayesian inference for the spectral density based on irregularly spaced data ⋮ Automatic estimation of spatial spectra via smoothing splines ⋮ Adaptive Bayesian Spectral Analysis of High-Dimensional Nonstationary Time Series ⋮ Time-varying spectral matrix estimation via intrinsic wavelet regression for surfaces of Hermitian positive definite matrices ⋮ AdaptSPEC-X: Covariate-Dependent Spectral Modeling of Multiple Nonstationary Time Series ⋮ Adaptive Bayesian Sum of Trees Model for Covariate-Dependent Spectral Analysis ⋮ Bayesian Model Search for Nonstationary Periodic Time Series ⋮ Efficient Bayesian PARCOR approaches for dynamic modeling of multivariate time series ⋮ A test for second-order stationarity of a time series based on the maximum of Anderson-Darling statistics ⋮ Fast Bayesian inference on spectral analysis of multivariate stationary time series

Uses Software

• astsa
• BayesDA
• beyondWhittle
• BayesSpec

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Improving SAMC using smoothing methods: Theory and applications to Bayesian model selection problems
• Time-dependent frequency domain principal components analysis of multichannel non-stationary signals
• Nonparametric spectral analysis with applications to seizure characterization using EEG time series
• Asymptotics in statistics: some basic concepts
• Adaptive spectral estimation for nonstationary multivariate time series
• A likelihood approximation for locally stationary processes
• Beyond Whittle: nonparametric correction of a parametric likelihood with a focus on Bayesian time series analysis
• Bayesian mixture modeling for spectral density estimation
• Spectral decompositions of multiple time series: a Bayesian non-parametric approach
• Estimation and information in stationary time series
• A unified framework for spline estimators
• Conditional adaptive Bayesian spectral analysis of nonstationary biomedical time series
• Estimating linear dependence between nonstationary time series using the locally stationary wavelet model
• Nonparametric Spectral Density Estimation Using Penalized Whittle Likelihood
• Adaptive Bayesian Wavelet Shrinkage
• Automatic Statistical Analysis of Bivariate Nonstationary Time Series
• Time-Dependent Spectral Analysis of Nonstationary Time Series
• Estimating Time-Evolving Partial Coherence Between Signals via Multivariate Locally Stationary Wavelet Processes
• Bayesian Estimation of the Spectral Density of a Time Series
• AdaptSPEC: Adaptive Spectral Estimation for Nonstationary Time Series
• Penalized multivariate Whittle likelihood for power spectrum estimation
• Automatic estimation of multivariate spectra via smoothing splines
• SLEX Analysis of Multivariate Nonstationary Time Series
• Structural Break Estimation for Nonstationary Time Series Models

This page was built for publication: Adaptive Bayesian Time–Frequency Analysis of Multivariate Time Series