Classification of multivariate non-stationary signals: the SLEX-shrinkage approach

DOI10.1016/j.jspi.2010.04.040zbMath1233.62128OpenAlexW2047022621MaRDI QIDQ993820

Jerome Sanes, Hilmar Böhm, Hernando Ombao, Rainer von Sachs

Publication date: 20 September 2010

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/2078.1/91105

zbMATH Keywords

discriminationshrinkagemultivariate time seriesSLEX librarySLEX spectrum

Mathematics Subject Classification ID

Related Items (4)

Variation pattern classification of functional data ⋮ Clustering nonlinear, nonstationary time series using BSLEX ⋮ The generalized shrinkage estimator for the analysis of functional connectivity of brain signals ⋮ A similarity measure for second order properties of non-stationary functional time series with applications to clustering and testing

Uses Software

• SLEX

Cites Work

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• Shrinkage estimation in the frequency domain of multivariate time series
• Fitting time series models to nonstationary processes
• The SLEX model of a non-stationary random process
• A likelihood approximation for locally stationary processes
• Discriminant analysis for locally stationary processes
• Mathematical Considerations in the Estimation of Spectra
• Discrimination and Clustering for Multivariate Time Series
• Ten Lectures on Wavelets
• Linear Discriminant Functions for Stationary Time Series
• Automatic Statistical Analysis of Bivariate Nonstationary Time Series
• Entropy-based algorithms for best basis selection
• Consistent Classification of Nonstationary Time Series Using Stochastic Wavelet Representations
• Discrimination and Classification of Nonstationary Time Series Using the SLEX Model
• SLEX Analysis of Multivariate Nonstationary Time Series

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