Statistical properties of a blind source separation estimator for stationary time series

DOI10.1016/j.spl.2012.06.025zbMath1312.62110OpenAlexW2018119392WikidataQ109772832 ScholiaQ109772832MaRDI QIDQ712509

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

Publication date: 17 October 2012

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2012.06.025

zbMATH Keywords

asymptotic normalityminimum distance indexAMUSEautocovariance matrixMA(\(\infty\)) processes

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Estimation in multivariate analysis (62H12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20)

Related Items (14)

A more efficient second order blind identification method for separation of uncorrelated stationary time series ⋮ Minimum distance index for complex valued ICA ⋮ Unnamed Item ⋮ Unnamed Item ⋮ Deflation-based separation of uncorrelated stationary time series ⋮ Blind source separation for spatial compositional data ⋮ Stationary subspace analysis based on second-order statistics ⋮ 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 ⋮ Separation of Uncorrelated Stationary time series using Autocovariance Matrices ⋮ On the usage of joint diagonalization in multivariate statistics ⋮ Modeling temporally uncorrelated components of complex-valued stationary processes

Uses Software

• JADE
• ICALAB

Cites Work

• Characteristics of multivariate distributions and the invariant coordinate system
• Multivariate versions of Bartlett's formula
• Indeterminacy and identifiability of blind identification
• Unnamed Item

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