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Copula-type estimators for flexible multivariate density modeling using mixtures
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Tran, Minh-Ngoc, Giordani, Paolo, Mun, Xiuyan, Kohn, Robert and Pitt, Michael K. (2014) Copula-type estimators for flexible multivariate density modeling using mixtures. Journal of Computational and Graphical Statistics, Volume 23 (Number 4). pp. 1163-1178. doi:10.1080/10618600.2013.842918 ISSN 1061-8600.
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Official URL: http://dx.doi.org/10.1080/10618600.2013.842918
Abstract
Copulas are popular as models for multivariate dependence because they allow the marginal densities and the joint dependence to be modeled separately. However, they usually require that the transformation from uniform marginals to the marginals of the joint dependence structure is known. This can only be done for a restricted set of copulas, e.g. a normal copula. Our article introduces copula-type estimators for flexible multivariate density estimation which also allow the marginal densities to be modeled separately from the joint dependence, as in copula modeling, but overcomes the lack of flexibility of most popular copula estimators. An iterative scheme is proposed for estimating copula-type estimators and its usefulness is demonstrated through simulation and real examples. The joint dependence is modeled by mixture of normals and mixture of normals factor analyzers models, and mixture of t and mixture of t factor analyzers models. We develop efficient Variational Bayes algorithms for fitting these in which model selection is performed automatically. Based on these mixture models, we construct four classes of copula-type densities which are far more flexible than current popular copula densities, and outperform them in a simulated data set and several real data sets. Supplementary material for this article is available online.
| Item Type: | Journal Article | ||||||
|---|---|---|---|---|---|---|---|
| Divisions: | Faculty of Social Sciences > Economics | ||||||
| Journal or Publication Title: | Journal of Computational and Graphical Statistics | ||||||
| Publisher: | American Statistical Association | ||||||
| ISSN: | 1061-8600 | ||||||
| Official Date: | 20 October 2014 | ||||||
| Dates: |
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| Volume: | Volume 23 | ||||||
| Number: | Number 4 | ||||||
| Page Range: | pp. 1163-1178 | ||||||
| DOI: | 10.1080/10618600.2013.842918 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Restricted or Subscription Access |
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