Separation of Uncorrelated Stationary time series using Autocovariance Matrices is a research paper published in Journal of Time Series Analysis (2015). On theSindex it has a DataRank of 0.546. It has been cited 37 times.
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.
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Base Score Contribution
0.546
From this paper's citation signal
Citation Network Contribution
0
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This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.