Bayesian semiparametric copula estimation with application to psychiatric genetics

DOI10.1002/bimj.201300130zbMath1325.62201OpenAlexW2112596331WikidataQ33867602 ScholiaQ33867602MaRDI QIDQ5257934

Wesley K. Thompson, Ori Rosen

Publication date: 24 June 2015

Published in: Biometrical Journal (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc5496008

zbMATH Keywords

Gaussian copulaschizophreniaB-spline densitiescardiovascular disease risk factors

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Markov processes: estimation; hidden Markov models (62M05) Monte Carlo methods (65C05) Genetics and epigenetics (92D10)

Related Items (1)

A Bayesian semiparametric Gaussian copula approach to a multivariate normality test

Cites Work

• Extending the rank likelihood for semiparametric copula estimation
• Goodness-of-fit tests for copulas: A review and a power study
• Efficient Bayesian inference for Gaussian copula regression models
• The meta-elliptical distributions with given marginals
• Modeling dropouts by conditional distribution, a copula-based approach
• Copula-based regression models: a survey
• Generating random correlation matrices based on partial correlations
• Modeling covariance matrices via partial autocorrelations
• Flexible smoothing with \(B\)-splines and penalties. With comments and a rejoinder by the authors
• Size, power and false discovery rates
• Density estimation and comparison with a penalized mixture approach
• Shrinkage Estimators for Covariance Matrices
• Smooth Random Effects Distribution in a Linear Mixed Model
• Joint Regression Analysis of Correlated Data Using Gaussian Copulas
• Density Estimation in the Presence of Heteroscedastic Measurement Error
• Multivariate Dispersion Models Generated From Gaussian Copula
• Genomic Control for Association Studies
• Estimation of Copula Models With Discrete Margins via Bayesian Data Augmentation
• Modeling Longitudinal Data Using a Pair-Copula Decomposition of Serial Dependence
• Inference in Semiparametric Dynamic Models for Binary Longitudinal Data
• Efficient Estimation of Semiparametric Multivariate Copula Models
• Remarks on a Multivariate Transformation
• Prior distributions for variance parameters in hierarchical models (Comment on article by Browne and Draper)

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