Extreme value analysis within a parametric outlier detection framework | Zendy

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Extreme value analysis within a parametric outlier detection framework

Author(s) -

Cabras S.,

Morales J.

Publication year - 2006

Publication title -

applied stochastic models in business and industry

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.413

H-Index - 40

eISSN - 1526-4025

pISSN - 1524-1904

DOI - 10.1002/asmb.660

Subject(s) - outlier , extreme value theory , parametric statistics , computer science , surprise , anomaly detection , selection (genetic algorithm) , parametric model , measure (data warehouse) , sample (material) , sample size determination , statistics , model selection , econometrics , data mining , artificial intelligence , mathematics , physics , psychology , social psychology , thermodynamics

Threshold selection is a key aspect in extreme values analysis, especially when the sample size is small. The main idea underpinning this work is that extreme observations are assumed to be outliers of a specified parametric model. We propose a threshold selection method based on outlier detection using a suitable measure of surprise. Copyright © 2006 John Wiley & Sons, Ltd.

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