# Objective Bayesian analysis for the multivariate skew-t model

## TL;DR

This paper introduces a Bayesian approach for the multivariate skew-t model, including a new parameterization, priors, and a sampler, with extensions to regression and frontier models, and provides an R package for implementation.

## Contribution

It presents a novel Bayesian framework for the multivariate skew-t model, extending previous skew-normal models and offering practical tools like an R package.

## Key findings

- Successful Bayesian inference for multivariate skew-t models
- Extension to regression and stochastic frontier models demonstrated
- Implementation via the mvst R package available

## Abstract

We perform a 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 & Parisi (2013) to a more realistic p-variate skew-t model. We also introduce the R package mvst, which allows to estimate the multivariate skew-t model.