Contributions to bayesian nonparametric and objective Bayes literature

Abstract

The thesis contributes to the literature on Bayesian nonparametrics by proposing two approaches, the first one related to time series analysis with a focus on sparsity of the matrix of coefficients and the second one to conditional copula models with an application to twin data. On the other hand, the thesis contributes to the literature on the analysis of the Yule-Simon distribution by proposing two objective priors on the parameter of the distribution and a Gibbs sampling algorithm for the analysis of the posterior distribution.