Bayesian lattice filters for time-varying autoregression and time-frequency analysis

DOI10.1214/15-BA978zbMath1359.62390arXiv1408.2757MaRDI QIDQ516530

Wen-Hsi Yang, Scott H. Holan, Christopher K. Wikle

Publication date: 14 March 2017

Published in: Bayesian Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1408.2757

zbMATH Keywords

model selectionnonstationarysequential estimationpartial autocorrelationlocally stationarypiecewise stationarytime-varying spectral density

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to environmental and related topics (62P12) Bayesian inference (62F15)

Related Items (5)

AdaptSPEC-X: Covariate-Dependent Spectral Modeling of Multiple Nonstationary Time Series ⋮ Bayesian circular lattice filters for computationally efficient estimation of multivariate time-varying autoregressive models ⋮ Efficient Bayesian PARCOR approaches for dynamic modeling of multivariate time series ⋮ The local partial autocorrelation function and some applications ⋮ Conditional adaptive Bayesian spectral analysis of nonstationary biomedical time series

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