Time-varying behavioral features and non-linear dependence are widely observed in big
data and challenge the operating systems and processes of risk management in financial …

Skew-symmetric distributions are a popular family of flexible distributions that conveniently
model non-normal features such as skewness, kurtosis and multimodality. Unfortunately …

The estimation problem of epsilon-skew-normal (ESN) distribution parameters is considered
within Bayesian approaches. This family of distributions contains the normal distribution, can …

Includes the following sections: The multivariate skew normal distribution; Full conditionals
and MCMC proposals; COMIX: default hyperpriors and parameters; Case study: sensitivity …

This article introduces a new asymmetric distribution constructed by assuming the
multivariate normal mean-variance mixture model. Called normal mean-variance mixture of …

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 …

Genomic selection (GS) has become a tool for selecting candidates in plant and animal
breeding programs. In the case of quantitative traits, it is common to assume that the …

This work aims to provide an environment for all users who are beginner in the context of the
statistical simulation approaches. These techniques are known as the Monte Carlo methods …

Periodic autoregressive (PAR) models with symmetric innovations are widely used on time
series analysis, whereas its asymmetric counterpart inference remains a challenge, because …

This dissertation builds a modeling framework for non-Gaussian spatial processes, time
series, and point processes, with a Bayesian inference paradigm that provides uncertainty …