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• W2893457630 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 “ https://ncsde.shinyapps.io/NCSDE ” is developed for visualization, training, and learning the SDFs collectively using the proposed technique. Finally, we apply our method to cluster similar brain signals recorded by the for identifying synchronized brain regions according to their spectral densities." @default.
• W2893457630 created "2018-10-05" @default.
• W2893457630 creator A5003955574 @default.
• W2893457630 creator A5010793158 @default.
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• W2893457630 date "2018-09-26" @default.
• W2893457630 modified "2023-09-24" @default.
• W2893457630 title "Nonparametric collective spectral density estimation with an application to clustering the brain signals" @default.
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• W2893457630 doi "https://doi.org/10.1002/sim.7972" @default.
• W2893457630 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30259540" @default.
• W2893457630 hasPublicationYear "2018" @default.
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