European Business Schools Librarian's Group

ESSEC Working Papers,
ESSEC Research Center, ESSEC Business School

No DR 10009: Statistiques des valeurs extrêmes dans le cas de lois discrètes

Anis Borchani ()
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Anis Borchani: ESSAI (Ecole Supérieure de la Statistique et de l’Analyse de l’Informatio), Tunis, Postal: 6 rue des Métiers, Charguia II 2035 , TUNIS, TUNISIA

Abstract: We propose a method to generate a warning system for the early detection of time clusters in discrete time series. Two approaches are developed, one using an approximation of the return period of an extreme event, independently of the nature of the data, the other using an estimation of the return period via standard EVT tools after a smoothing of our discrete data into continuous ones. This method allows us to define a surveillance and prediction system which is applied to finance and public health surveillance

Keywords: applications in insurance and finance; clusters; epidemiology; Extreme Value Theory; extreme quantile; outbreak detection; return level; return period; surveillance

JEL-codes: C22; I10

88 pages, December 2010

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