CMStatistics 2017: Start Registration
View Submission - CMStatistics
Title: Bayesian nonparametric conditional copula estimation of twin data Authors:  Luca Rossini - Vrije Universiteit Amsterdam (Netherlands) [presenting]
Fabrizio Leisen - University of Kent (United Kingdom)
Luciana Dalla Valle - University of Plymouth (United Kingdom)
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, the purpose is to correctly analyse the influence of the socioeconomic status on the relationship between twins' cognitive abilities. The methodology is based on conditional copulas, which allow us to model the effect of a covariate driving the strength of dependence between the main variables. We propose a flexible Bayesian nonparametric approach for the estimation of conditional copulas, which can model any conditional copula density. The methodology extends previous work by introducing dependence from a covariate in an infinite mixture model. The results suggest that environmental factors are more influential in families with lower socio-economic position.