Nonparametric collective spectral density estimation with an application to clustering the brain signals
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Online Publication Date2018-09-26
Print Publication Date2018-12-30
Permanent link to this recordhttp://hdl.handle.net/10754/628862
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AbstractIn 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
CitationMaadooliat 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.
SponsorsWe 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.
JournalStatistics in Medicine