Multivariate Bayesian analysis of Gaussian, right censored Gaussian, ordered categorical and binary traits using Gibbs sampling

• Main Authors: Madsen Per, Gianola Daniel, Sorensen Daniel, Lund Mogens, Korsgaard Inge, Jensen Just
• Format: Article
• Language: deu
• Published: BMC 2003-03-01
• Series: Genetics Selection Evolution
• Subjects: categorical; Gaussian; multivariate Bayesian analysis; right censored Gaussian
• Online Access: http://www.gsejournal.org/content/35/2/159

<p>Abstract</p> <p>A fully Bayesian analysis using Gibbs sampling and data augmentation in a multivariate model of Gaussian, right censored, and grouped Gaussian traits is described. The grouped Gaussian traits are either ordered categorical traits (with more than two categories) or binary traits, where the grouping is determined <it>via </it>thresholds on the underlying Gaussian scale, the liability scale. Allowances are made for unequal models, unknown covariance matrices and missing data. Having outlined the theory, strategies for implementation are reviewed. These include joint sampling of location parameters; efficient sampling from the fully conditional posterior distribution of augmented data, a multivariate truncated normal distribution; and sampling from the conditional inverse Wishart distribution, the fully conditional posterior distribution of the residual covariance matrix. Finally, a simulated dataset was analysed to illustrate the methodology. This paper concentrates on a model where residuals associated with liabilities of the binary traits are assumed to be independent. A Bayesian analysis using Gibbs sampling is outlined for the model where this assumption is relaxed.</p>