Bayesian Non-Parametric Conditional Copula Estimation of Twin Data
提出一种贝叶斯非参数条件Copula估计方法，用于分析社会经济地位如何影响双胞胎认知能力之间的依赖关系，发现环境因素在低社会经济地位家庭中影响更大。
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.