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

Mehdi Maadooliat, Ying Sun, Tianbo Chen

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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
Original languageEnglish (US)
Pages (from-to)4789-4806
Number of pages18
JournalStatistics in Medicine
Volume37
Issue number30
DOIs
StatePublished - Sep 26 2018

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