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



: Miettinen J, Nordhausen K, Oja H, Taskinen S

Publisher: ELSEVIER SCIENCE BV

: 2012

: Statistics and Probability Letters

: STATISTICS & PROBABILITY LETTERS

: STAT PROBABIL LETT

: 82

: 11

: 1865

: 1873

: 9

: 0167-7152

DOI: https://doi.org/10.1016/j.spl.2012.06.025


In this paper, we assume that the observed p time series are linear combinations of p latent uncorrelated weakly stationary time series. The problem is then, using the observed p-variate time series, to find an estimate for a mixing or unmixing matrix for the combinations. The estimated uncorrelated time series may then have nice interpretations and can be used in a further analysis. The popular AMUSE algorithm finds an estimate of an unmixing matrix using covariances and autocovariances of the observed time series. In this paper, we derive the limiting distribution of the AMUSE estimator under general conditions, and show how the results can be used for the comparison of estimates. The exact formula for the limiting covariance matrix of the AMUSE estimate is given for general MA(infinity) processes. (C) 2012 Elsevier B.V. All rights reserved.



Last updated on 2024-26-11 at 16:17