Comparison of Parametric and Nonparametric Methods for Estimation of Hydrological Extremes
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F13%3APU104671" target="_blank" >RIV/00216305:26210/13:PU104671 - isvavai.cz</a>
Výsledek na webu
<a href="https://www.tu-braunschweig.de/Medien-DB/stochastik/bridges-abstracts.pdf" target="_blank" >https://www.tu-braunschweig.de/Medien-DB/stochastik/bridges-abstracts.pdf</a>
DOI - Digital Object Identifier
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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Comparison of Parametric and Nonparametric Methods for Estimation of Hydrological Extremes
Popis výsledku v původním jazyce
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.
Název v anglickém jazyce
Comparison of Parametric and Nonparametric Methods for Estimation of Hydrological Extremes
Popis výsledku anglicky
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.
Klasifikace
Druh
O - Ostatní výsledky
CEP obor
BB - Aplikovaná statistika, operační výzkum
OECD FORD obor
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Návaznosti výsledku
Projekt
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Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2013
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů