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    Stationary subspace analysis of nonstationary covariance processes: eigenstructure description and testing

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    Type
    Preprint
    Authors
    Sundararajan, Raanju Ragavendar
    Pipiras, Vladas
    Pourahmadi, Mohsen
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2019-04-20
    Permanent link to this record
    http://hdl.handle.net/10754/660833
    
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    Abstract
    Stationary subspace analysis (SSA) searches for linear combinations of the components of nonstationary vector time series that are stationary. These linear combinations and their number defne an associated stationary subspace and its dimension. SSA is studied here for zero mean nonstationary covariance processes. We characterize stationary subspaces and their dimensions in terms of eigenvalues and eigenvectors of certain symmetric matrices. This characterization is then used to derive formal statistical tests for estimating dimensions of stationary subspaces. Eigenstructure-based techniques are also proposed to estimate stationary subspaces, without relying on previously used computationally intensive optimization-based methods. Finally, the introduced methodologies are examined on simulated and real data.
    Sponsors
    The second author was supported in part by the National Science Foundation grant DMS-1712966. The work of the third author was supported by the National Science Foundation grant DMS-1612984.
    Publisher
    arXiv
    arXiv
    1904.09420
    Additional Links
    https://arxiv.org/pdf/1904.09420
    Collections
    Preprints; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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