Separation of Uncorrelated Stationary time series using Autocovariance Matrices

Miettinen J, Illner K, Nordhausen K, Oja H, Taskinen S, Theis FJ (2016)

Publication Type: Journal article

Publication year: 2016

Journal

Journal of Time Series Analysis Wiley-Blackwell

Book Volume: 37

Pages Range: 337-354

Journal Issue: 3

DOI: 10.1111/jtsa.12159

Abstract

In blind source separation, one assumes that the observed p time series are linear combinations of p latent uncorrelated weakly stationary time series. To estimate the unmixing matrix, which transforms the observed time series back to uncorrelated latent time series, second-order blind identification (SOBI) uses joint diagonalization of the covariance matrix and autocovariance matrices with several lags. In this article, we find the limiting distribution of the well-known symmetric SOBI estimator under general conditions and compare its asymptotical efficiencies to those of the recently introduced deflation-based SOBI estimator. The theory is illustrated by some finite-sample simulation studies.

Involved external institutions

University of Jyväskylä / Jyväskylän yliopisto FI Finland (FI) Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich DE Germany (DE) University of Turku / Turun yliopisto FI Finland (FI) Technische Universität München (TUM) DE Germany (DE)

How to cite

APA:

Miettinen, J., Illner, K., Nordhausen, K., Oja, H., Taskinen, S., & Theis, F.J. (2016). Separation of Uncorrelated Stationary time series using Autocovariance Matrices. Journal of Time Series Analysis, 37(3), 337-354. https://doi.org/10.1111/jtsa.12159

MLA:

Miettinen, Jari, et al. "Separation of Uncorrelated Stationary time series using Autocovariance Matrices." Journal of Time Series Analysis 37.3 (2016): 337-354.

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