Bayesian multiscale feature detection of log-spectral densities

Sigrunn H. Sørbye*, Kristian Hindberg, Lena R. Olsen, Håvard Rue

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

A fully-automatic Bayesian visualization tool to identify periodic components of evenly sampled stationary time series, is presented. The given method applies the multiscale ideas of the SiZer-methodology to the log-spectral density of a given series. The idea is to detect significant peaks in the true underlying curve viewed at different resolutions or scales. The results are displayed in significance maps, illustrating for which scales and for which frequencies, peaks in the log-spectral density are detected as significant. The inference involved in producing the significance maps is performed using the recently developed simplified Laplace approximation. This is a Bayesian deterministic approach used to get accurate estimates of posterior marginals for latent Gaussian Markov random fields at a low computational cost, avoiding the use of Markov chain Monte Carlo techniques. Application of the given exploratory tool is illustrated analyzing both synthetic and real time series.

Original languageEnglish (US)
Pages (from-to)3746-3754
Number of pages9
JournalComputational Statistics and Data Analysis
Volume53
Issue number11
DOIs
StatePublished - Sep 1 2009
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Theory and Mathematics
  • Computational Mathematics
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Bayesian multiscale feature detection of log-spectral densities'. Together they form a unique fingerprint.

Cite this