Bayesian non-parametric conditional copula estimation of twin data

11Citations
Citations of this article
16Readers
Mendeley users who have this article in their library.

This artice is free to access.

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.

Cite

CITATION STYLE

APA

Dalla Valle, L., Leisen, F., & Rossini, L. (2018). Bayesian non-parametric conditional copula estimation of twin data. Journal of the Royal Statistical Society. Series C: Applied Statistics, 67(3), 523–548. https://doi.org/10.1111/rssc.12237

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free