Name
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s10260-017-0404-0 | Statistical Methods and Applications |
Keywords | Field | DocType |
Multivariate skew-t model,Multivariate skew-normal model,Objective Bayes inference,Population Monte Carlo sampler,Skewness | Econometrics,T-model,Multivariate statistics,Skew,Novelty,Statistics,Multivariate analysis,Prior probability,Bivariate analysis,Mathematics,Bayesian probability | Journal |
Volume | Issue | ISSN |
27 | 2 | 1618-2510 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antonio Parisi | 1 | 2 | 1.16 |
| Brunero Liseo | 2 | 3 | 2.52 |