Bayesian Nonparametric Modeling of Conditional Multidimensional Dependence Structures
Bayesian Nonparametric Modeling of Conditional Multidimensional Dependence Structures
复制标题
条件多维依赖结构的贝叶斯非参数建模
DOI:
10.1080/10618600.2023.2173604
复制
发表时间:
2023
影响因子:
2.4
通讯作者:
Barone R
中科院分区:
文献类型:
--
作者:
Barone R
In recent years, conditional copulas, that allow dependence between variables to vary according to the values of one or more covariates, have attracted increasing attention. However, the literature mainly focused on the bivariate case, since the constraints on the multivariate copulas correlation matrices would make the specifications of covariates arduous. In high dimension, vine copulas offer greater flexibility compared to multivariate copulas, since they are constructed using bivariate copulas as building blocks. We present a novel inferential approach for multivariate distributions, which combines the flexibility of vine constructions with the advantages of Bayesian nonparametrics, not requiring the specification of parametric families for each pair copula. Expressing multivariate copulas using vines allows us to easily account for covariate specifications driving the dependence between response variables. We specify the vine copula density as an infinite mixture of Gaussian copulas, defining a Dirichlet process prior on the mixing measure, and performing posterior inference via Markov chain Monte Carlo sampling. Our approach is successful as for clustering as well as for density estimation. We carry out simulation studies and apply the proposed approach to analyze a veterinary dataset and to investigate the impact of natural disasters on financial development. Supplementary materials for this article are available online.
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DOI:
--
发表时间:
2014
期刊:
影响因子:
--
作者:
Jim E. Griffin;F. Leisen
通讯作者:
F. Leisen
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作者:
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DOI:
--
发表时间:
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期刊:
影响因子:
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通讯作者:
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通讯作者:
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通讯作者:
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