Copula based factorization in Bayesian multivariate infinite mixture models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11230%2F14%3A10227277" target="_blank" >RIV/00216208:11230/14:10227277 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.jmva.2014.02.011" target="_blank" >http://dx.doi.org/10.1016/j.jmva.2014.02.011</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jmva.2014.02.011" target="_blank" >10.1016/j.jmva.2014.02.011</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Copula based factorization in Bayesian multivariate infinite mixture models

Popis výsledku v původním jazyce

Bayesian nonparametric models based on infinite mixtures of density kernels have been recently gaining in popularity due to their flexibility and feasibility of implementation even in complicated modeling scenarios. However, these models have been rarelyapplied in more than one dimension. Indeed, implementation in the multivariate case is inherently difficult due to the rapidly increasing number of parameters needed to characterize the joint dependence structure accurately. In this paper, we propose afactorization scheme of multivariate dependence structures based on the copula modeling framework, whereby each marginal dimension in the mixing parameter space is modeled separately and the marginals are then linked by a nonparametric random copula function. Specifically, we consider nonparametric univariate Gaussian mixtures for the marginals and a multivariate random Bernstein polynomial copula for the link function, under the Dirichlet process prior. We show that in a multivariate se

Název v anglickém jazyce

Copula based factorization in Bayesian multivariate infinite mixture models

Popis výsledku anglicky

Bayesian nonparametric models based on infinite mixtures of density kernels have been recently gaining in popularity due to their flexibility and feasibility of implementation even in complicated modeling scenarios. However, these models have been rarelyapplied in more than one dimension. Indeed, implementation in the multivariate case is inherently difficult due to the rapidly increasing number of parameters needed to characterize the joint dependence structure accurately. In this paper, we propose afactorization scheme of multivariate dependence structures based on the copula modeling framework, whereby each marginal dimension in the mixing parameter space is modeled separately and the marginals are then linked by a nonparametric random copula function. Specifically, we consider nonparametric univariate Gaussian mixtures for the marginals and a multivariate random Bernstein polynomial copula for the link function, under the Dirichlet process prior. We show that in a multivariate se

Druh

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

CEP obor

AH - Ekonomie

OECD FORD obor

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Projekt

—

Návaznosti

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název periodika

Journal of Multivariate Analysis

ISSN

0047-259X

e-ISSN

—

Svazek periodika

127

Číslo periodika v rámci svazku

MAY 2014

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

200-213

Kód UT WoS článku

000334819700015

EID výsledku v databázi Scopus

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