Nonparametric collective spectral density estimation with an application to clustering the brain signals.
Stat Med
; 37(30): 4789-4806, 2018 12 30.
Article
en En
| MEDLINE
| ID: mdl-30259540
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Encéfalo
/
Análisis por Conglomerados
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Estadísticas no Paramétricas
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Electroencefalografía
Tipo de estudio:
Prognostic_studies
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Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Stat Med
Año:
2018
Tipo del documento:
Article