An approach to structure determination and estimation of hierarchical Archimedean Copulas and its application to Bayesian classification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F47813059%3A19520%2F15%3A%230003540" target="_blank" >RIV/47813059:19520/15:#0003540 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985807:_____/16:00442862

Výsledek na webu

<a href="http://link.springer.com/article/10.1007/s10844-014-0350-3" target="_blank" >http://link.springer.com/article/10.1007/s10844-014-0350-3</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10844-014-0350-3" target="_blank" >10.1007/s10844-014-0350-3</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An approach to structure determination and estimation of hierarchical Archimedean Copulas and its application to Bayesian classification

Popis výsledku v původním jazyce

Copulas are distribution functions with standard uniform univariate marginals. Copulas are widely used for studying dependence among continuously distributed random variables, with applications in finance and quantitative risk management; see, e.g., thepricing of collateralized debt obligations. The ability to model complex dependence structures among variables has recently become increasingly popular in the realm of statistics, one example being data mining (e.g., cluster analysis, evolutionary algorithms or classification). The present work considers an estimator for both the structure and the parameters of hierarchical Archimedean copulas. Such copulas have recently become popular alternatives to the widely used Gaussian copulas. The proposed estimator is based on a pairwise inversion of Kendall's tau estimator recently considered in the literature but can be based on other estimators as well, such as likelihood-based. A simple algorithm implementing the proposed estimator is provi

Název v anglickém jazyce

An approach to structure determination and estimation of hierarchical Archimedean Copulas and its application to Bayesian classification

Popis výsledku anglicky

Copulas are distribution functions with standard uniform univariate marginals. Copulas are widely used for studying dependence among continuously distributed random variables, with applications in finance and quantitative risk management; see, e.g., thepricing of collateralized debt obligations. The ability to model complex dependence structures among variables has recently become increasingly popular in the realm of statistics, one example being data mining (e.g., cluster analysis, evolutionary algorithms or classification). The present work considers an estimator for both the structure and the parameters of hierarchical Archimedean copulas. Such copulas have recently become popular alternatives to the widely used Gaussian copulas. The proposed estimator is based on a pairwise inversion of Kendall's tau estimator recently considered in the literature but can be based on other estimators as well, such as likelihood-based. A simple algorithm implementing the proposed estimator is provi

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GA13-17187S" target="_blank" >GA13-17187S: Konstrukce pokročilých srozumitelných klasifikátorů</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název periodika

Journal of Intelligent Information Systems

ISSN

0925-9902

e-ISSN

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Svazek periodika

46

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

39

Strana od-do

21-59

Kód UT WoS článku

000372261600002

EID výsledku v databázi Scopus

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