A more efficient second order blind identification method for separation of uncorrelated stationary time series
Abstract
The classical second order source separation methods use approximate joint diagonalization of autocovariance matrices with several lags to estimate the unmixing matrix. Based on recent asymptotic results, we propose a novel unmixing matrix estimator which selects the best lag set from a finite set of candidate sets specified by the user. The theory is illustrated by a simulation study.
Main Authors
Format
Articles
Research article
Published
2016
Series
Subjects
Publication in research information system
Publisher
Elsevier BV
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-201605172593Use this for linking
Review status
Peer reviewed
ISSN
0167-7152
DOI
https://doi.org/10.1016/j.spl.2016.04.007
Language
English
Published in
Statistics and Probability Letters
Citation
- Taskinen, S., Miettinen, J., & Nordhausen, K. (2016). A more efficient second order blind identification method for separation of uncorrelated stationary time series. Statistics and Probability Letters, 116, 21-26. https://doi.org/10.1016/j.spl.2016.04.007
License
Copyright© 2016 Elsevier B.V. This is a final draft version of an article whose final and definitive form has been published by Elsevier. Published in this repository with the kind permission of the publisher.