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    Nonparametric collective spectral density estimation with an application to clustering the brain signals

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    Type
    Article
    Authors
    Maadooliat, Mehdi
    Sun, Ying cc
    Chen, Tianbo cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics Program
    Date
    2018-09-26
    Online Publication Date
    2018-09-26
    Print Publication Date
    2018-12-30
    Permanent link to this record
    http://hdl.handle.net/10754/628862
    
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    Abstract
    In this paper, we develop a method for the simultaneous estimation of spectral density functions (SDFs) for a collection of stationary time series that share some common features. Due to the similarities among the SDFs, the log-SDF can be represented using a common set of basis functions. The basis shared by the collection of the log-SDFs is estimated as a low-dimensional manifold of a large space spanned by a prespecified rich basis. A collective estimation approach pools information and borrows strength across the SDFs to achieve better estimation efficiency. Moreover, each estimated spectral density has a concise representation using the coefficients of the basis expansion, and these coefficients can be used for visualization, clustering, and classification purposes. The Whittle pseudo-maximum likelihood approach is used to fit the model and an alternating blockwise Newton-type algorithm is developed for the computation. A web-based shiny App found at
    Citation
    Maadooliat M, Sun Y, Chen T (2018) Nonparametric collective spectral density estimation with an application to clustering the brain signals. Statistics in Medicine. Available: http://dx.doi.org/10.1002/sim.7972.
    Sponsors
    We would like to thank two anonymous referees for their constructive and thoughtful comments, which helped us tremendously in revising the manuscript. The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST) to Ying Sun and Tianbo Chen.
    Publisher
    Wiley
    Journal
    Statistics in Medicine
    DOI
    10.1002/sim.7972
    Additional Links
    https://onlinelibrary.wiley.com/doi/full/10.1002/sim.7972
    ae974a485f413a2113503eed53cd6c53
    10.1002/sim.7972
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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