6533b854fe1ef96bd12adf91

RESEARCH PRODUCT

A more efficient second order blind identification method for separation of uncorrelated stationary time series

Klaus NordhausenKlaus NordhausenJari MiettinenSara Taskinen

subject

Statistics and ProbabilityMathematical optimizationaffine equivarianceminimum distance indexasymptotic normalityAsymptotic distributionlinear process01 natural sciencesSet (abstract data type)010104 statistics & probabilityMatrix (mathematics)SOBIComputingMethodologies_SYMBOLICANDALGEBRAICMANIPULATION0502 economics and businessSource separationjoint diagonalization0101 mathematicsFinite set050205 econometrics Mathematicsta112Series (mathematics)05 social sciencesEstimatorAutocovarianceStatistics Probability and UncertaintyAlgorithm

description

The classical second order source separation methods use approximate joint diagonalization of autocovariance matrices with several lags to estimate the unmixing matrix. Based on recent asymptotic results, we propose a novel unmixing matrix estimator which selects the best lag set from a finite set of candidate sets specified by the user. The theory is illustrated by a simulation study.

yearjournalcountryeditionlanguage
2016-09-01Statistics & Probability Letters
https://doi.org/10.1016/j.spl.2016.04.007