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Computational aspects of Bayesian spectral density estimation

Liseo, Brunero; Rousseau, Judith; Chopin, Nicolas (2013), Computational aspects of Bayesian spectral density estimation, Journal of Computational and Graphical Statistics, 22, 3, p. 533-557. http://dx.doi.org/10.1080/10618600.2013.785293

Type
Article accepté pour publication ou publié
External document link
http://hal.archives-ouvertes.fr/hal-00767466
Date
2013
Journal name
Journal of Computational and Graphical Statistics
Volume
22
Number
3
Publisher
Taylor and Francis; Taylor and Francis
Pages
533-557
Publication identifier
http://dx.doi.org/10.1080/10618600.2013.785293
Metadata
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Author(s)
Liseo, Brunero
Rousseau, Judith
Chopin, Nicolas
Abstract (EN)
Gaussian time-series models are often specified through their spectral density. Such models pose several computational challenges, in particular because of the non-sparse nature of the covariance matrix. We derive a fast approximation of the likelihood for such models. We use importance sampling to correct for the approximation error. We show that the variance of the importance sampling weights vanishes as the sample size goes to infinity. We show that the posterior is typically multi-modal, and derive a Sequential Monte Carlo sampler based on an annealing sequence in order to sample from the approximate posterior. Performance of the overall approach is evaluated on simulated and real datasets.
Subjects / Keywords
Sequential Monte Carlo; Long memory processes; FEXP
JEL
C15 - Statistical Simulation Methods: General
C11 - Bayesian Analysis: General

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