Empirical copula for missing observations

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F13%3A10159514" target="_blank" >RIV/00216208:11320/13:10159514 - isvavai.cz</a>

Výsledek na webu

<a href="https://mme2013.vspj.cz/about-conference/conference-proceedings" target="_blank" >https://mme2013.vspj.cz/about-conference/conference-proceedings</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Empirical copula for missing observations

Popis výsledku v původním jazyce

Empirical copula (EC) is a rank-based method for estimating the dependence structure of a random vector, which is convenient when someone wants to estimate non-parametrically the whole distribution of the random vector in two separated stages: rst the margins and then the dependence structure fully represented by a copula. The discrete nature of an EC can be successively smoothed using Bernstein polynomials approximation to obtain di fferentiable estimate of underlying copula or its density. In practice, however, some components of some observations are often missing. In this case the marginal distributions of individual vector components can be estimated without any changes using all available information, whilst the second step is not so straightforward. One can employ only complete observations to capture the dependence but then the mapping of estimated copula on individual marginal quantiles is not reflected appropriately. In the article the author suggests to generalize the classi

Název v anglickém jazyce

Empirical copula for missing observations

Popis výsledku anglicky

Empirical copula (EC) is a rank-based method for estimating the dependence structure of a random vector, which is convenient when someone wants to estimate non-parametrically the whole distribution of the random vector in two separated stages: rst the margins and then the dependence structure fully represented by a copula. The discrete nature of an EC can be successively smoothed using Bernstein polynomials approximation to obtain di fferentiable estimate of underlying copula or its density. In practice, however, some components of some observations are often missing. In this case the marginal distributions of individual vector components can be estimated without any changes using all available information, whilst the second step is not so straightforward. One can employ only complete observations to capture the dependence but then the mapping of estimated copula on individual marginal quantiles is not reflected appropriately. In the article the author suggests to generalize the classi

Druh

D - Stať ve sborníku

CEP obor

BA - Obecná matematika

OECD FORD obor

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Projekt

<a href="/cs/project/GA13-01930S" target="_blank" >GA13-01930S: Robustní procedury pro nestandardní situace, jejich diagnostika a implementace</a><br>

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

Název statě ve sborníku

Proceedings of the 31st International Conference on Mathematical Methods in Economics 2013

ISBN

978-80-87035-76-4

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

482-487

Název nakladatele

College of Polytechnics, Jihlava, Czech Republic

Místo vydání

Jihlava

Místo konání akce

Jihlava

Datum konání akce

11. 9. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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