Details

Autor(en) / Beteiligte

• Martos, Gabriel
• Hernández, Nicolás
• Muñoz, Alberto
• Moguerza, Javier

Titel

Entropy Measures for Stochastic Processes with Applications in Functional Anomaly Detection

Ist Teil von

• Entropy (Basel, Switzerland), 2018-01, Vol.20 (1), p.33

Ort / Verlag

Basel: MDPI AG

Erscheinungsjahr

2018

Link zum Volltext

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• 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.