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Bayesian non-parametric conditional copula estimation of twin data

Dalla Valle, Luciana, Leisen, Fabrizio, Rossini, Luca (2018) Bayesian non-parametric conditional copula estimation of twin data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 67 (3). pp. 523-548. ISSN 0035-9254. E-ISSN 1467-9876. (doi:10.1111/rssc.12237)

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https://dx.doi.org/10.1111/rssc.12237

Abstract

Several studies on heritability in twins aim at understanding the different contribution of environmental and genetic factors to specific traits. Considering the national merit twin study, our purpose is to analyse correctly the influence of socio-economic status on the relationship between twins’ cognitive abilities. Our methodology is based on conditional copulas, which enable us to model the effect of a covariate driving the strength of dependence between the main variables. We propose a flexible Bayesian non-parametric approach for the estimation of conditional copulas, which can model any conditional copula density. Our methodology extends the work of Wu, Wang and Walker in 2015 by introducing dependence from a covariate in an infinite mixture model. Our results suggest that environmental factors are more influential in families with lower socio-economic position.

Item Type: Article
DOI/Identification number: 10.1111/rssc.12237
Uncontrolled keywords: Bayesian non-parametrics; Conditional copula models; National merit twin study;Slice sampling; Social science
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science
Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Fabrizio Leisen
Date Deposited: 16 Aug 2017 14:26 UTC
Last Modified: 15 Jan 2020 15:36 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/62861 (The current URI for this page, for reference purposes)
Leisen, Fabrizio: https://orcid.org/0000-0002-2460-6176
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