A Bayesian nonparametric mixture model for grouping dependence structures and selecting copula functions

Haoxin Zhuang, Liqun Diao and Grace Y. Yi

Econometrics and Statistics, 2022, vol. 22, issue C, 172-189

Abstract: The demand for advanced dependence modeling arises in a variety of fields, including finance, insurance and health science. When analyzing dependent data, it is important but challenging to properly model the dependence structure in order to carry out valid and efficient inferences. Grouping the data according to the similarity in the dependence structure is necessary, especially for data of a small size. A copula-based model, indexed by copula selection indicators and dependence parameters, is introduced to delineate dependent data and group similar dependence structures. To conduct inference, a Bayesian nonparametric method with the prior distributions specified as a Dirichlet Process is proposed as a mixture of Dirichlet process mixture copula model (M-DPM-CM). Extensive simulation studies have been conducted to evaluate the performance of the proposed procedure, and the results show that the proposed M-DPM-CM can recover the true grouping structure and achieve high accuracy in copula model selection under various finite sample settings. The M-DPM-CM is applied to analyze the Vertebral Column dataset from UCI Machine Learning Repository.

Keywords: Copula; Dependence Modeling; Dirichlet Process; Grouping; MCMC (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:22:y:2022:i:c:p:172-189

DOI: 10.1016/j.ecosta.2021.03.009

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