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dc.creatorAndrade, Larissa Ribeiro de-
dc.creatorFerreira, Daniel Furtado-
dc.creatorSáfadi, Thelma-
dc.creatorBarroso, Lúcia Pereira-
dc.identifier.citationANDRADE, L. R. de et al. Bayesian analysis of dynamic factor models using multivariate T distribution. Revista Brasileira de Biometria, Lavras, v. 36, n. 1, p. 140-156, mar. 2018.pt_BR
dc.description.abstractThe multivariate t models are symmetric and have heavier tail than the normal distribution and produce robust inference procedures for applications. In this paper, the Bayesian estimation of a dynamic factor model is presented, where the factors follow a multivariate autoregressive model, using the multivariate t distribution. Since the multivariate t distribution is complex, it was represented in this work as a mix of the multivariate normal distribution and a square root of a chi-square distribution. This method allowed the complete dene of all the posterior distributions. The inference on the parameters was made taking a sample of the posterior distribution through a Gibbs Sampler. The convergence was veried through graphical analysis and the convergence diagnostics of Geweke (1992) and Raftery and Lewis (1992).pt_BR
dc.publisherUniversidade Federal de Lavraspt_BR
dc.sourceRevista Brasileira de Biometriapt_BR
dc.subjectFactor modelspt_BR
dc.subjectGibbs samplerpt_BR
dc.subjectMultivariate t.pt_BR
dc.subjectModelos de fatorpt_BR
dc.subjectAmostragem Gibbspt_BR
dc.subjectMultivariada t.pt_BR
dc.titleBayesian analysis of dynamic factor models using multivariate T distributionpt_BR
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