Deflation-based separation of uncorrelated stationary time series

DOI10.1016/j.jmva.2013.09.009zbMath1278.62147OpenAlexW2036273657WikidataQ109744494 ScholiaQ109744494MaRDI QIDQ391930

Klaus Nordhausen, Sara Taskinen, Hannu Oja, Jari Petteri Miettinen

Publication date: 13 January 2014

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2013.09.009

zbMATH Keywords

asymptotic normalityblind source separationminimum distance indexSOBIautocovarianceMA\((\infty)\) processes

Mathematics Subject Classification ID

Related Items (10)

A more efficient second order blind identification method for separation of uncorrelated stationary time series ⋮ Unnamed Item ⋮ Blind source separation for spatial compositional data ⋮ New independent component analysis tools for time series ⋮ Extracting Conditionally Heteroskedastic Components using Independent Component Analysis ⋮ On robustifying some second order blind source separation methods for nonstationary time series ⋮ Blind source separation for compositional time series ⋮ Sliced average variance estimation for multivariate time series ⋮ Separation of Uncorrelated Stationary time series using Autocovariance Matrices ⋮ On the usage of joint diagonalization in multivariate statistics

Uses Software

• FastICA
• ICALAB

Cites Work

• Statistical properties of a blind source separation estimator for stationary time series
• Multivariate versions of Bartlett's formula
• Time series: theory and methods.
• Portfolio value at risk based on independent component analysis
• Independent Component Analysis Involving Autocorrelated Sources With an Application to Functional Magnetic Resonance Imaging
• Blind Separation of Gaussian Sources With General Covariance Structures: Bounds and Optimal Estimation
• Jacobi Angles for Simultaneous Diagonalization
• Separation of an Instantaneous Mixture of Gaussian Autoregressive Sources by the Exact Maximum Likelihood Approach

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