Generalized additive modelling of hydrological sample extremes
Seminar Room 1, Newton Institute
AbstractCo-authors: Anthony Davison (EPFL, Lausanne), Marius Hofert (ETHZ, Zurich) Estimation of flood frequencies and severities is important for many water management issues. We present a smoothing extreme value method fitted by penalized loglikelihood. Spline smoothing is used to estimate the parameters of the frequency and size distributions of extremes, depending on covariates in a non- or semiparametric way. The frequency process of high level extremes is modelled by a Poisson process, either homogeneous or non-homogeneous. The extreme sizes are considered to follow a generalized Pareto distribution. Being given by two parameters, the method of spline smoothing is not straightforward to apply. An efficient fitting algorithm based on orthogonal reparametrisation is developed to achieve this task. The method is applied to the daily maximum flows of an hydrological station in Switzerland and is used to estimate 20-year return levels.
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