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.
| Language | English |
|---|---|
| Pages | 523-548 |
| Number of pages | 26 |
| Journal | Journal of the Royal Statistical Society. Series C: Applied Statistics |
| Volume | 67 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 1 Apr 2018 |
| Externally published | Yes |
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Keywords
- Bayesian non-parametrics
- Conditional copula models
- National merit twin study
- Slice sampling
- Social science
Cite this
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Bayesian non-parametric conditional copula estimation of twin data. / Dalla Valle, Luciana; Leisen, Fabrizio; Rossini, Luca.
In: Journal of the Royal Statistical Society. Series C: Applied Statistics, Vol. 67, No. 3, 01.04.2018, p. 523-548.Research output: Contribution to Journal › Article › Academic › peer-review
TY - JOUR
T1 - Bayesian non-parametric conditional copula estimation of twin data
AU - Dalla Valle, Luciana
AU - Leisen, Fabrizio
AU - Rossini, Luca
PY - 2018/4/1
Y1 - 2018/4/1
N2 - 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.
AB - 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.
KW - Bayesian non-parametrics
KW - Conditional copula models
KW - National merit twin study
KW - Slice sampling
KW - Social science
UR - http://www.scopus.com/inward/record.url?scp=85042285069&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85042285069&partnerID=8YFLogxK
U2 - 10.1111/rssc.12237
DO - 10.1111/rssc.12237
M3 - Article
VL - 67
SP - 523
EP - 548
JO - Journal of the Royal Statistical Society: Series C (Applied Statistics)
T2 - Journal of the Royal Statistical Society: Series C (Applied Statistics)
JF - Journal of the Royal Statistical Society: Series C (Applied Statistics)
SN - 0035-9254
IS - 3
ER -