2014/05/14 by Jari Miettinen, J.K. Miettinen, Katrin Illner +4 · 55 citations
Biochemistry, Genetics and Molecular Biology · Chemistry · Computer Science · Mathematics · #Algorithm #Applied mathematics #Autocovariance #Blind Source Separation Techniques #Blind signal separation #Computer science #Covariance #Covariance matrix #Estimator #Fractal and DNA sequence analysis #Mathematical analysis #Mathematics #Series (stratigraphy) #Spectroscopy and Chemometric Analyses #Statistics #Uncorrelated #math.ST #msc:62H05 #msc:62H10 #stat.TH
paper · pdf · doi:10.1111/jtsa.12159
published in Journal of Time Series Analysis 37(3), 337-354 (Wiley)
arxiv created 2014/05/14 · openalex publication_date 2015/09/09 · arxiv updated 2017/09/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.