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• W4311451326 abstract "Various nonparametric approaches for Bayesian spectral density estimation of stationary time series have been suggested in the literature, mostly based on the Whittle likelihood approximation. A generalization of this approximation involving a nonparametric correction of a parametric likelihood has been proposed in the literature with a proof of posterior consistency for spectral density estimation in combination with the Bernstein–Dirichlet process prior for Gaussian time series. In this article, we will extend the posterior consistency result to non-Gaussian time series by employing a general consistency theorem for dependent data and misspecified models. As a special case, posterior consistency for the spectral density under the Whittle likelihood is also extended to non-Gaussian time series. Small sample properties of this approach are illustrated with several examples of non-Gaussian time series." @default.
• W4311451326 created "2022-12-26" @default.
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• W4311451326 date "2023-01-13" @default.
• W4311451326 modified "2023-10-18" @default.
• W4311451326 title "Posterior consistency for the spectral density of non‐Gaussian stationary time series" @default.
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• W4311451326 doi "https://doi.org/10.1111/sjos.12627" @default.
• W4311451326 hasPublicationYear "2023" @default.
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