Bayesian mixture modeling for spectral density estimation

Annalisa Cadonna, Athanasios Kottas and Raquel Prado

Statistics & Probability Letters, 2017, vol. 125, issue C, 189-195

Abstract: We develop a Bayesian modeling approach for spectral densities built from a local Gaussian mixture approximation to the Whittle log-likelihood. The implied model for the log-spectral density is a mixture of linear functions with frequency-dependent logistic weights, which allows for general shapes for smooth spectral densities. The proposed approach facilitates efficient posterior simulation as it casts the spectral density estimation problem in a mixture modeling framework for density estimation. The methodology is illustrated with synthetic and real data sets.

Keywords: Logistic mixture weights; Markov chain Monte Carlo; Normal mixtures; Whittle likelihood (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (4)

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DOI: 10.1016/j.spl.2017.02.008

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