Copula based factorization in Bayesian multivariate infinite mixture models

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Copula based factorization in Bayesian multivariate infinite mixture models
Original language description
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
Czech name
—
Czech description
—

Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
AH - Economics
OECD FORD branch
—

Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)