Penalized quasi-maximum likelihood estimation for extreme value models with application to flood frequency analysis

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dc.contributor.authorBücher, Axel
dc.contributor.authorLilienthal, Jona
dc.contributor.authorKinsvater, Paul
dc.contributor.authorFried, Roland
dc.date.accessioned2021-03-11T08:04:56Z
dc.date.available2021-03-11T08:04:56Z
dc.date.issued2020-06-03
dc.description.abstractA common statistical problem in hydrology is the estimation of annual maximal river flow distributions and their quantiles, with the objective of evaluating flood protection systems. Typically, record lengths are short and estimators imprecise, so that it is advisable to exploit additional sources of information. However, there is often uncertainty about the adequacy of such information, and a strict decision on whether to use it is difficult. We propose penalized quasi-maximum likelihood estimators to overcome this dilemma, allowing one to push the model towards a reasonable direction defined a priori. We are particularly interested in regional settings, with river flow observations collected at multiple stations. To account for regional information, we introduce a penalization term inspired by the popular Index Flood assumption. Unlike in standard approaches, the degree of regionalization can be controlled gradually instead of deciding between a local or a regional estimator. Theoretical results on the consistency of the estimator are provided and extensive simulations are performed for the reason of comparison with other local and regional estimators. The proposed procedure yields very good results, both for homogeneous as well as for heterogeneous groups of sites. A case study consisting of sites in Saxony, Germany, illustrates the applicability to real data.en
dc.identifier.urihttp://hdl.handle.net/2003/40074
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-21951
dc.language.isoende
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectRegionalizationen
dc.subjectIndex flood assumptionen
dc.subjectGeneralized extreme value distributionen
dc.subjectConsistency with rateen
dc.subjectTuning parameter selectionen
dc.subject.ddc310
dc.titlePenalized quasi-maximum likelihood estimation for extreme value models with application to flood frequency analysisen
dc.typeTextde
dc.type.publicationtypearticleen
dcterms.accessRightsopen access
eldorado.secondarypublicationtruede
eldorado.secondarypublication.primarycitationBücher, A., Lilienthal, J., Kinsvater, P. et al. Penalized quasi-maximum likelihood estimation for extreme value models with application to flood frequency analysis. Extremes (2020).de
eldorado.secondarypublication.primaryidentifierhttps://doi.org/10.1007/s10687-020-00379-yde

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Lehrstuhl Mathematische Statistik und naturwissenschaftliche Anwendungen