title: Bayesian semiparametric copula estimation with application to psychiatric genetics.
abstract: 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.
authors: Department of Mathematical Sciences, University of Texas at El Paso, El Paso, TX, 79968, USA.
authors: Ori
authors: O
authors: Rosen
authors: Wesley K
authors: WK
authors: Thompson
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D002318:Cardiovascular Diseases / Q000175:diagnosis / Q000453:epidemiology* / Q000235:genetics*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D020022:Genetic Predisposition to Disease / Q000453:epidemiology / Q000235:genetics*; D006801:Humans; D015994:Incidence; D015233:Models, Statistical; D016011:Normal Distribution; D012307:Risk Factors; D012559:Schizophrenia / Q000175:diagnosis / Q000453:epidemiology* / Q000235:genetics*
keywords: B-spline densities; Cardiovascular disease risk factors; Gaussian copula; Schizophrenia