A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Separation of Uncorrelated Stationary time series using Autocovariance Matrices
Tekijät: Jari Miettinen, Katrin Illner, Klaus Nordhausen, Hannu Oja, Sara Taskinen, Fabian J. Theis
Kustantaja: WILEY-BLACKWELL
Julkaisuvuosi: 2016
Journal: Journal of Time Series Analysis
Tietokannassa oleva lehden nimi: JOURNAL OF TIME SERIES ANALYSIS
Lehden akronyymi: J TIME SER ANAL
Vuosikerta: 37
Numero: 3
Aloitussivu: 337
Lopetussivu: 354
Sivujen määrä: 18
ISSN: 0143-9782
DOI: https://doi.org/10.1111/jtsa.12159
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.