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Entropy Measures for Stochastic Processes with Applications in Functional Anomaly Detection.


ABSTRACT: 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.

SUBMITTER: Martos G 

PROVIDER: S-EPMC7512230 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Entropy Measures for Stochastic Processes with Applications in Functional Anomaly Detection.

Martos Gabriel G   Hernández Nicolás N   Muñoz Alberto A   Moguerza Javier M JM  

Entropy (Basel, Switzerland) 20180111 1


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 inte  ...[more]

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