Bayesian non‐parametric conditional copula estimation of twin data. Issue 3 (8th August 2017)

• Record Type: Journal Article
• Authors: Valle, Luciana Dalla; Leisen, Fabrizio; Rossini, Luca
• Is Part Of: Journal of the Royal Statistical Society. Volume 67:Issue 3(2018:May)
• Page Start: 523
• Page End: 548
• Publication Date: 2017-08-08
• Subjects: Bayesian non‐parametrics -- Conditional copula models -- National merit twin study -- Slice sampling -- Social science; Statistics -- Periodicals; 519.5
• DOI: 10.1111/rssc.12237
• Languages: English
• ISSNs: 0035-9254

Abstract

Summary: 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.