Statistiques des valeurs extrêmes dans le cas de lois discrètes

Anis Borchani ()
Additional contact information
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, http://www.cns.tn/plateforme/detail.php?id=56

No DR 10009, ESSEC Working Papers from ESSEC Research Center, ESSEC Business School

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 (search for similar items in EconPapers)
JEL-codes: C22 I10 (search for similar items in EconPapers)
Pages: 88 pages
Date: 2010-12
New Economics Papers: this item is included in nep-ecm, nep-ias and nep-rmg
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hal-essec.archives-ouvertes.fr/docs/00/57/25/59/PDF/10009.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ebg:essewp:dr-10009

Access Statistics for this paper

More papers in ESSEC Working Papers from ESSEC Research Center, ESSEC Business School ESSEC Research Center, BP 105, 95021 Cergy, France. Contact information at EDIRC.
Bibliographic data for series maintained by Sophie Magnanou ().