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Bayesian comparison of forecasting models to expected progenies difference in Nelore cattle genetic breeding

The objective of this work was to accomplish a bayesian analysis of an autoregressive, AR(p), panel data model from Nelore sires' expected progenie difference (EPD) observed during 2000-2006. The AR(2) model was used due to the results of partial autocorrelation function analysis. The prior comparisons were performed through Bayes Factor and Pseudo-Bayes Factor, and the results showed the independent t-Student multivariate - inverse Gamma superiority in relation to the hierarchical multivariate Normal - inverse Gamma and Jeffreys prior. Results indicate the importance of sires grouping by accuracy values, and also show forecast efficiency around 80%.

MCMC algorithm; panel data; autoregressive model


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