Artículo
Entropy Measures for Stochastic Processes with Applications in Functional Anomaly Detection
Fecha de publicación:
01/2018
Editorial:
Molecular Diversity Preservation International
Revista:
Entropy
ISSN:
1099-4300
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
We propose a definition of entropy for stochastic processes. We provide a reproducing kernel Hilbert space model to estimate entropy from a random sample of realizations of a stochastic process, namely functional data, and introduce two approaches to estimate minimum entropy sets. These sets are relevant to detect anomalous or outlier functional data. A numerical experiment illustrates the performance of the proposed method; in addition, we conduct an analysis of mortality rate curves as an interesting application in a real-data context to explore functional anomaly detection.
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Colecciones
Articulos(OCA CIUDAD UNIVERSITARIA)
Articulos de OFICINA DE COORDINACION ADMINISTRATIVA CIUDAD UNIVERSITARIA
Articulos de OFICINA DE COORDINACION ADMINISTRATIVA CIUDAD UNIVERSITARIA
Citación
Martos, Gabriel Alejandro; Hernández, Nicolás; Muñoz, Alberto; Moguerza, Javier; Entropy Measures for Stochastic Processes with Applications in Functional Anomaly Detection; Molecular Diversity Preservation International; Entropy; 20; 1; 1-2018
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