Open Access
2020 A Bayesian hierarchical copula model
Haoxin Zhuang, Liqun Diao, Grace Y. Yi
Electron. J. Statist. 14(2): 4457-4488 (2020). DOI: 10.1214/20-EJS1784


Dependent data with hierarchical structures arises commonly from a variety of application, and analysis of such data is often challenging due to the complexity in modeling dependence structures and the computation intensity. In this paper, we propose a Bayesian hierarchical copula model (BHCM) to accommodate hierarchical structures of dependent data, where the subject-level dependence is modeled by the copula-based model and the hierarchical structure is described using random dependence parameters. We introduce a layer-by-layer sampling scheme for conducting Bayesian inferences. Our proposed BHCM enjoys the flexibility of modeling various complex association structures, while retaining manageable computation. Extensive simulation studies show that our proposed estimators outperform conventional likelihood-based estimators in a variety of finite sample settings. We apply the BHCM to analyze the Vertebral Column dataset arising from UCI Machine Learning Repository.


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Haoxin Zhuang. Liqun Diao. Grace Y. Yi. "A Bayesian hierarchical copula model." Electron. J. Statist. 14 (2) 4457 - 4488, 2020.


Received: 1 July 2020; Published: 2020
First available in Project Euclid: 31 December 2020

MathSciNet: MR4194268
Digital Object Identifier: 10.1214/20-EJS1784

Keywords: Bayesian hierarchical model , copula , dependence modeling , MCMC

Vol.14 • No. 2 • 2020
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