Objective Bayesian analysis for the multivariate skew-t model

Antonio Parisi and B. Liseo
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B. Liseo: Sapienza Università di Roma

Statistical Methods & Applications, 2018, vol. 27, issue 2, No 8, 277-295

Abstract: Abstract We propose a novel Bayesian analysis of the p-variate skew-t model, providing a new parameterization, a set of non-informative priors and a sampler specifically designed to explore the posterior density of the model parameters. Extensions, such as the multivariate regression model with skewed errors and the stochastic frontiers model, are easily accommodated. A novelty introduced in the paper is given by the extension of the bivariate skew-normal model given in Liseo and Parisi (2013) to a more realistic p-variate skew-t model. We also introduce the R package mvst, which produces a posterior sample for the parameters of a multivariate skew-t model.

Keywords: Multivariate skew-t model; Multivariate skew-normal model; Objective Bayes inference; Population Monte Carlo sampler; Skewness (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10260-017-0404-0

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