Quotation Cadonna, Annalisa, Kottas, Athanasios, Prado, Raquel. 2017. Bayesian mixture modeling for spectral density estimation. Statistics and Probability Letters 125, 189-195.




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.


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Publication's profile

Status of publication Published
Affiliation External
Type of publication Journal article
Journal Statistics and Probability Letters
Citation Index SCI
Language English
Title Bayesian mixture modeling for spectral density estimation
Volume 125
Year 2017
Page from 189
Page to 195
Reviewed? Y
DOI http://dx.doi.org/10.1016/j.spl.2017.02.008


Cadonna, Annalisa (Former researcher)
Prado, Raquel (University of California, Santa Cruz, United States/USA)
Kottas, Athanasios (university of California, Santa Cruz, United States/USA)
Institute for Statistics and Mathematics IN (Details)
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