Comparison of Parametric and Nonparametric Estimates of Extreme Value Distribution

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F13%3APU105190" target="_blank" >RIV/00216305:26210/13:PU105190 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Comparison of Parametric and Nonparametric Estimates of Extreme Value Distribution

Popis výsledku v původním jazyce

The presented paper is focused on comparison of different approaches to estimation of parameters of extreme value distributions. The commonly used parametric methods are compared with several nonparametric approaches, and properties of the estimates arediscussed. The parametric inference is based on the partial duration series method and the generalized Pareto distribution. Unknown parameters of the distribution are estimated using the maximum likelihood method, and the method of probability weighted moments, which are often used in hydrology. The nonparametric inference is based on results presented by Gomes and Oliveira (see [1]), and the tail index of the extreme value distribution is estimated using the bootstrap methodology. The performance of estimators is compared using real and simulated data. The real data consists of historical rainfall series in the form of rainfall intensities from six stations operated by the Czech Hydrometeorological Institute in South Moravian Region in

Název v anglickém jazyce

Comparison of Parametric and Nonparametric Estimates of Extreme Value Distribution

Popis výsledku anglicky

The presented paper is focused on comparison of different approaches to estimation of parameters of extreme value distributions. The commonly used parametric methods are compared with several nonparametric approaches, and properties of the estimates arediscussed. The parametric inference is based on the partial duration series method and the generalized Pareto distribution. Unknown parameters of the distribution are estimated using the maximum likelihood method, and the method of probability weighted moments, which are often used in hydrology. The nonparametric inference is based on results presented by Gomes and Oliveira (see [1]), and the tail index of the extreme value distribution is estimated using the bootstrap methodology. The performance of estimators is compared using real and simulated data. The real data consists of historical rainfall series in the form of rainfall intensities from six stations operated by the Czech Hydrometeorological Institute in South Moravian Region in

Druh

O - Ostatní výsledky

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů