This HTML5 document contains 41 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
dctermshttp://purl.org/dc/terms/
n14http://localhost/temp/predkladatel/
n5http://linked.opendata.cz/resource/domain/vavai/riv/tvurce/
n16http://linked.opendata.cz/resource/domain/vavai/subjekt/
n15http://linked.opendata.cz/ontology/domain/vavai/
rdfshttp://www.w3.org/2000/01/rdf-schema#
skoshttp://www.w3.org/2004/02/skos/core#
n3http://linked.opendata.cz/ontology/domain/vavai/riv/
n10http://linked.opendata.cz/resource/domain/vavai/vysledek/RIV%2F00216305%3A26210%2F13%3APU104671%21RIV14-MSM-26210___/
n2http://linked.opendata.cz/resource/domain/vavai/vysledek/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n4http://linked.opendata.cz/ontology/domain/vavai/riv/klicoveSlovo/
n18http://linked.opendata.cz/ontology/domain/vavai/riv/duvernostUdaju/
xsdhhttp://www.w3.org/2001/XMLSchema#
n12http://linked.opendata.cz/ontology/domain/vavai/riv/aktivita/
n8http://linked.opendata.cz/ontology/domain/vavai/riv/jazykVysledku/
n13http://linked.opendata.cz/ontology/domain/vavai/riv/druhVysledku/
n11http://linked.opendata.cz/ontology/domain/vavai/riv/obor/
n17http://reference.data.gov.uk/id/gregorian-year/

Statements

Subject Item
n2:RIV%2F00216305%3A26210%2F13%3APU104671%21RIV14-MSM-26210___
rdf:type
n15:Vysledek skos:Concept
rdfs:seeAlso
https://www.tu-braunschweig.de/Medien-DB/stochastik/bridges-abstracts.pdf
dcterms:description
In connection with the more frequent occurrence of extreme flood events in the Czech Republic, there is an increased interest in methods for modelling hydrological extremes. One of the commonly used methods is based on partial duration series and generalized Pareto distribution. Parameters of the distribution are usually estimated using parametric methods, e.g. maximum likelihood method and method of probability weighted moments. Another approach is based on nonparametric methods, where the tail index of the extreme value distribution is estimated using the bootstrap methodology. This contribution is focused on comparison of various approaches to estimation of parameters and parametric functions of extreme value distributions. Performance of the estimators is illustrated using real and/or simulated data. In connection with the more frequent occurrence of extreme flood events in the Czech Republic, there is an increased interest in methods for modelling hydrological extremes. One of the commonly used methods is based on partial duration series and generalized Pareto distribution. Parameters of the distribution are usually estimated using parametric methods, e.g. maximum likelihood method and method of probability weighted moments. Another approach is based on nonparametric methods, where the tail index of the extreme value distribution is estimated using the bootstrap methodology. This contribution is focused on comparison of various approaches to estimation of parameters and parametric functions of extreme value distributions. Performance of the estimators is illustrated using real and/or simulated data.
dcterms:title
Comparison of Parametric and Nonparametric Methods for Estimation of Hydrological Extremes Comparison of Parametric and Nonparametric Methods for Estimation of Hydrological Extremes
skos:prefLabel
Comparison of Parametric and Nonparametric Methods for Estimation of Hydrological Extremes Comparison of Parametric and Nonparametric Methods for Estimation of Hydrological Extremes
skos:notation
RIV/00216305:26210/13:PU104671!RIV14-MSM-26210___
n15:predkladatel
n16:orjk%3A26210
n3:aktivita
n12:S
n3:aktivity
S
n3:dodaniDat
n17:2014
n3:domaciTvurceVysledku
n5:2837633 n5:8030723 n5:6788319
n3:druhVysledku
n13:O
n3:duvernostUdaju
n18:S
n3:entitaPredkladatele
n10:predkladatel
n3:idSjednocenehoVysledku
66277
n3:idVysledku
RIV/00216305:26210/13:PU104671
n3:jazykVysledku
n8:eng
n3:klicovaSlova
extreme value distribution, parametric estimates, nonparametric estimates, maximum likelihood method, method of proability weighted moments, bootstrap, IDF curves
n3:klicoveSlovo
n4:parametric%20estimates n4:nonparametric%20estimates n4:extreme%20value%20distribution n4:bootstrap n4:method%20of%20proability%20weighted%20moments n4:IDF%20curves n4:maximum%20likelihood%20method
n3:kontrolniKodProRIV
[C7F81E92BCAF]
n3:obor
n11:BB
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
4
n3:rokUplatneniVysledku
n17:2013
n3:tvurceVysledku
Holešovský, Jan Fusek, Michal Michálek, Jaroslav Blachut, Vít
n14:organizacniJednotka
26210