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• W2144824550 abstract "Abstract A fully Bayesian method for quantitative genetic analysis of data consisting of ranks of, e.g., genotypes, scored at a series of events or experiments is presented. The model postulates a latent structure, with an underlying variable realized for each genotype or individual involved in the event. The rank observed is assumed to reflect the order of the values of the unobserved variables, i.e., the classical Thurstonian model of psychometrics. Parameters driving the Bayesian hierarchical model include effects of covariates, additive genetic effects, permanent environmental deviations, and components of variance. A Markov chain Monte Carlo implementation based on the Gibbs sampler is described, and procedures for inferring the probability of yet to be observed future rankings are outlined. Part of the model is rendered nonparametric by introducing a Dirichlet process prior for the distribution of permanent environmental effects. This can lead to potential identification of clusters of such effects, which, in some competitions such as horse races, may reflect forms of undeclared preferential treatment." @default.
• W2144824550 created "2016-06-24" @default.
• W2144824550 creator A5082768576 @default.
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• W2144824550 date "2006-11-01" @default.
• W2144824550 modified "2023-10-17" @default.
• W2144824550 title "A Thurstonian Model for Quantitative Genetic Analysis of Ranks: A Bayesian Approach" @default.
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• W2144824550 doi "https://doi.org/10.1534/genetics.106.060673" @default.
• W2144824550 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/1667069" @default.
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