Bayesian interpretation of periodograms

The usual nonparametric approach to spectral analysis is revisited within the regularization framework. Both usual and windowed periodograms are obtained as the squared modulus of the minimizer of regularized least squares criteria. Then, particular attention is paid to their interpretation within t...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on signal processing 2001-07, Vol.49 (7), p.1388-1396
Main Authors: Giovannelli, J.-F., Idier, J.
Format: Article
Language:English
Subjects:
Amplitude estimation
Applied sciences
Bayesian analysis
Bayesian methods
Criteria
Exact sciences and technology
Fourier transforms
Frequency
Information, signal and communications theory
Least squares method
Least squares methods
Maximum likelihood estimation
Regularization
Shape
Signal analysis
Signal and communications theory
Signal processing
Signal representation. Spectral analysis
Signal, noise
Spectra
Spectral analysis
Telecommunications and information theory
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The usual nonparametric approach to spectral analysis is revisited within the regularization framework. Both usual and windowed periodograms are obtained as the squared modulus of the minimizer of regularized least squares criteria. Then, particular attention is paid to their interpretation within the Bayesian statistical framework. Finally, the question of unsupervised hyperparameter and window selection is addressed. It is shown that maximum likelihood solution is both formally achievable and practically useful.
ISSN:1053-587X
1941-0476
DOI:10.1109/78.928692