A review of second‐order blind identification methods

Author:

Pan Yan1ORCID,Matilainen Markus2ORCID,Taskinen Sara1ORCID,Nordhausen Klaus13ORCID

Affiliation:

1. Department of Mathematics and Statistics University of Jyväskylä Finland

2. Turku PET Centre Turku University Hospital and University of Turku Finland

3. Institute of Statistics and Mathematical Methods in Economics, TU Vienna Austria

Funder

Austrian Science Fund

Publisher

Wiley

Subject

Statistics and Probability

Reference107 articles.

1. Time series factor models;Ensor K. B.;WIREs: Computational Statistics,2013

2. Additional general references for BSS for the interested reader are Hyvärinen et al. (2001); Cichocki and Amari (2002); Chabriel et al. (2014). Joint diagonalization that is used to solve the BSS problem can also be used for other data structures and not just for time series data. Joint diagonalization is used for iid tensorial data in Virta et al. (2017 2018) for functional data in Virta et al. (2020a) and for graph data in Miettinen Vorobyov and Ollila (2020).

3. Robust BSS approaches especially for the iid setting are discussed in Nordhausen et al. (2008); Hallin and Mehta (2015). More noise settings in the iid and time series setting are considered in Nordhausen et al. (2017); Pfister et al. (2019).

4. Spatial blind source separation;Bachoc F.;Biometrika,2020

5. Determining the number of factors in approximate factor models;Bai J.;Econometrica,2002

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