Estimation of spectral density of a stationary time series via an asymptotic of the periodogram

DOI10.1016/S0378-3758(98)00148-7zbMath0933.62094MaRDI QIDQ1298961

V. Pereyra

Publication date: 2 April 2000

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

zbMATH Keywords

spectral density estimationreversible jump MCMCpiecewise polynomial

Mathematics Subject Classification ID

Inference from stochastic processes and spectral analysis (62M15) Numerical analysis or methods applied to Markov chains (65C40)

Related Items (7)

Optimally adaptive Bayesian spectral density estimation for stationary and nonstationary processes ⋮ Bayesian spectral density estimation using P-splines with quantile-based knot placement ⋮ Posterior consistency for the spectral density of non‐Gaussian stationary time series ⋮ Spectral decompositions of multiple time series: a Bayesian non-parametric approach ⋮ Beyond Whittle: nonparametric correction of a parametric likelihood with a focus on Bayesian time series analysis ⋮ Bayesian nonparametric analysis of multivariate time series: a matrix gamma process approach ⋮ Bayesian nonparametric spectral density estimation using B-spline priors

Uses Software

• sapa

Cites Work

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• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Spectrum estimation by wavelet thresholding of multitaper estimators
• Spectral Analysis for Physical Applications
• CROSS-VALIDATORY CHOICE OF A SPECTRUM ESTIMATE AND ITS CONNECTIONS WITH AIC
• Automatic Smoothing of the Log Periodogram
• Efficient bias corrected nonparametric spectral estimation
• Some recent advances in time series modeling
• LOGSPLINE ESTIMATION OF A POSSIBLY MIXED SPECTRAL DISTRIBUTION

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