An efficient MCMC algorithm is presented to cluster the nodes of a network such that nodes with similar role in the network are clustered together. This is known as block-modeling or block-clustering. The model is the stochastic blockmodel (SBM) with block parameters integrated out. The...

In this paper we introduce a Random Walk test for Functional Autoregressive Processes of Order One. The test is non parametric, based on Bootstrap and Functional Principal Components. The power of the test is shown through an extensive Montecarlo simulation. We apply the test to two real...

Bayes estimates are derived in multivariate linear models with unknown distribution. The prior distribution is defined using a Dirichlet prior for the unknown error distribution and a ormal-Wishart distribution for the parameters. The posterior distribution for the parameters is determined and...

The location-scale model is usually present in physics and chemistry in connection to the Birge ratio method for the adjustment of fundamental physical constants such as the Planck constant or the Newtonian constant of gravitation, while the random effects model is the commonly used approach for...

We propose a specification test for a wide range of parametric models for the conditional distribution function of an outcome variable given a vector of covariates. The test is based on the Cramer-von Mises distance between an unrestricted estimate of the joint distribution function of the data,...

The reference prior algorithm (Berger and Bernardo, 1992) is applied to locationscale models with any regular sampling density. A number of two-sample problems is analyzed in this general context, extending the dierence, ratio and product of Normal means problems outside Normality, while...