Bayesian mixture modeling for spectral density estimation

DOI10.1016/j.spl.2017.02.008zbMath1380.62237OpenAlexW2588602095MaRDI QIDQ2407784

Athanasios Kottas, Annalisa Cadonna, Raquel Prado

Publication date: 6 October 2017

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2017.02.008

zbMATH Keywords

Markov chain Monte Carlonormal mixturesWhittle likelihoodlogistic mixture weights

Mathematics Subject Classification ID

Density estimation (62G07) Bayesian inference (62F15) Inference from stochastic processes and spectral analysis (62M15)

Related Items (8)

Brain waves analysis via a non-parametric Bayesian mixture of autoregressive kernels ⋮ Bayesian spectral density estimation using P-splines with quantile-based knot placement ⋮ Posterior consistency for the spectral density of non‐Gaussian stationary time series ⋮ Beyond Whittle: nonparametric correction of a parametric likelihood with a focus on Bayesian time series analysis ⋮ Bayesian Spectral Modeling for Multiple Time Series ⋮ Bayesian nonparametric analysis of multivariate time series: a matrix gamma process approach ⋮ Spectral density-based and measure-preserving ABC for partially observed diffusion processes. An illustration on Hamiltonian SDEs ⋮ Adaptive Bayesian Time–Frequency Analysis of Multivariate Time Series

Uses Software

• BayesLogit

Cites Work

• Approximation of conditional densities by smooth mixtures of regressions
• Hierarchical mixtures-of-experts for exponential family regression models: Approximation and maximum likelihood estimation
• A Note on Whittle's Likelihood
• Automatic Smoothing of the Log Periodogram
• Nonparametric Spectral Density Estimation Using Penalized Whittle Likelihood
• Bayesian Estimation of the Spectral Density of a Time Series
• Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables
• Automatic estimation of multivariate spectra via smoothing splines
• On Estimation of a Probability Density Function and Mode
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