Bayesian Nonparametric Modeling of Conditional Multidimensional Dependence Structures

Bayesian Nonparametric Modeling of Conditional Multidimensional Dependence Structures
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条件多维依赖结构的贝叶斯非参数建模

DOI:
10.1080/10618600.2023.2173604
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发表时间:
2023
影响因子:
2.4
通讯作者:
Barone R
Barone R
中科院分区:
数学2区
文献类型:
--
作者:
Barone R

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近年来,允许变量之间的依赖关系根据一个或多个协变量的值而变化的条件Copula引起了越来越多的关注。然而,文献主要集中在双变量的情况下,因为对多元Copula相关矩阵的约束会使协变量的规格化变得困难。在高维中,vine copula比多元copula提供更大的灵活性,因为它们是使用二元copula作为构建块来构建的。我们提出了一种新的多元分布的推理方法,它结合了葡萄树结构的灵活性与贝叶斯非参数的优点,不需要指定的参数家庭的每对Copula。使用葡萄树表达多变量Copula使我们能够轻松地解释驱动响应变量之间依赖性的协变量规范。我们指定葡萄树copula密度作为高斯copula的无限混合,定义一个狄利克雷过程之前的混合措施,并通过马尔可夫链蒙特卡罗采样进行后验推断。我们的方法是成功的聚类以及密度估计。我们进行了模拟研究,并应用所提出的方法来分析兽医数据集,并调查自然灾害对金融发展的影响。本文的补充材料可在网上查阅。
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.
复合随机测量及其在贝叶斯非参数中的应用
DOI: --
发表时间: 2014
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作者:
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发表时间: 2019-06-01
期刊: BIOMETRICS
影响因子: 1.9
作者:
Barthel, Nicole;Geerdens, Candida;Janssen, Paul
通讯作者: Janssen, Paul
DOI: 10.1007/s11009-013-9348-5
发表时间: 2014-09
影响因子: 0.9
作者:
Juan Wu;Xue Wang;S. Walker
通讯作者: Juan Wu;Xue Wang;S. Walker