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SUMMARY:Generalized additive modelling of hydrological sample extremes - C
 havez-Demoulin\, V (ETH Zrich)
DTSTART:20131031T145000Z
DTEND:20131031T152500Z
UID:TALK48628@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:Co-authors: Anthony Davison (EPFL\, Lausanne)\, Marius Hofert 
 (ETHZ\, Zurich)\n\nEstimation of flood frequencies and severities is impor
 tant for many water management issues. We present a smoothing extreme valu
 e method fitted by penalized loglikelihood. Spline smoothing is used to es
 timate the parameters of the frequency and size distributions of extremes\
 , depending on covariates in a non- or semiparametric way. The frequency p
 rocess of high level extremes is modelled by a Poisson process\, either ho
 mogeneous or non-homogeneous. The extreme sizes are considered to follow a
  generalized Pareto distribution. Being given by two parameters\, the meth
 od of spline smoothing is not straightforward to apply. An efficient fitti
 ng algorithm based on orthogonal reparametrisation is developed to achieve
  this task. The method is applied to the daily maximum flows of an hydrolo
 gical station in Switzerland and is used to estimate 20-year return levels
 .\n
LOCATION:Seminar Room 1\, Newton Institute
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