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Bayesian semiparametric copula estimation with application to psychiatric genetics
Author(s) -
Rosen Ori,
Thompson Wesley K.
Publication year - 2015
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201300130
Subject(s) - frequentist inference , marginal distribution , copula (linguistics) , econometrics , conditional probability distribution , conditional dependence , markov chain monte carlo , multivariate statistics , mathematics , statistics , inference , multivariate normal distribution , bayesian probability , bayesian inference , computer science , artificial intelligence , random variable
This paper proposes a semiparametric methodology for modeling multivariate and conditional distributions. We first build a multivariate distribution whose dependence structure is induced by a Gaussian copula and whose marginal distributions are estimated nonparametrically via mixtures of B‐spline densities. The conditional distribution of a given variable is obtained in closed form from this multivariate distribution. We take a Bayesian approach, using Markov chain Monte Carlo methods for inference. We study the frequentist properties of the proposed methodology via simulation and apply the method to estimation of conditional densities of summary statistics, used for computing conditional local false discovery rates, from genetic association studies of schizophrenia and cardiovascular disease risk factors.