Bayesian non-parametric conditional copula estimation of twin data

Luciana Dalla Valle, Fabrizio Leisen, Luca Rossini

Research output: Contribution to JournalArticleAcademicpeer-review

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.

Original languageEnglish
Pages (from-to)523-548
Number of pages26
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Volume67
Issue number3
DOIs
Publication statusPublished - 1 Apr 2018
Externally publishedYes

Fingerprint

Bayesian Nonparametrics
Copula
Covariates
Economics
Heritability
Methodology
Environmental Factors
Mixture Model
Model

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 JournalArticleAcademicpeer-review

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