Generalization of geometric median

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F13%3A10190212" target="_blank" >RIV/00216208:11320/13:10190212 - 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

Generalization of geometric median

Popis výsledku v původním jazyce

In the paper are suggested new robust estimators of location and variance. It is proved that these estimators have a breakdown point one half. The used method comes from a geometric median. In the first step it is shown that we can employ one half of observations and the estimate stays robust in the sense of the breakdown point. In the second step we show that we can add even more observations which are in some sense close to the geometric median and still get robust results. The robustness is proved inboth steps for a multidimensional case. Since we can employ more observations and stay robust in the sense of the breakdown point, we enlarge the used information in comparison to other robust estimators like median and therefore get better results. Wecombine the advantage of the robust estimator and the classical mean. Our estimators are compared by simulation study with classical estimators like mean, median or alpha windsorised estimator. The comparison is done for different distrib

Název v anglickém jazyce

Generalization of geometric median

Popis výsledku anglicky

In the paper are suggested new robust estimators of location and variance. It is proved that these estimators have a breakdown point one half. The used method comes from a geometric median. In the first step it is shown that we can employ one half of observations and the estimate stays robust in the sense of the breakdown point. In the second step we show that we can add even more observations which are in some sense close to the geometric median and still get robust results. The robustness is proved inboth steps for a multidimensional case. Since we can employ more observations and stay robust in the sense of the breakdown point, we enlarge the used information in comparison to other robust estimators like median and therefore get better results. Wecombine the advantage of the robust estimator and the classical mean. Our estimators are compared by simulation study with classical estimators like mean, median or alpha windsorised estimator. The comparison is done for different distrib

Druh

D - Stať ve sborníku

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

Název statě ve sborníku

Proceedings of the 31st International Conference 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

25-30

Název nakladatele

College of Polytechnics Jihlava

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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