Statistiques des valeurs extrêmes dans le cas de lois discrètes
AbstractWe 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
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Bibliographic InfoPaper provided by ESSEC Research Center, ESSEC Business School in its series ESSEC Working Papers with number DR 10009.
Length: 88 pages
Date of creation: Dec 2010
Date of revision:
applications in insurance and finance; clusters; epidemiology; Extreme Value Theory; extreme quantile; outbreak detection; return level; return period; surveillance;
Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- I10 - Health, Education, and Welfare - - Health - - - General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-03-19 (All new papers)
- NEP-ECM-2011-03-19 (Econometrics)
- NEP-IAS-2011-03-19 (Insurance Economics)
- NEP-RMG-2011-03-19 (Risk Management)
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