mcmc.gesm

An object type list, which provides the setup (i.e., values of hyperparameters, initial values, model matrices, basis functions for
the B-splines, burn-in and posterior sample size) of the model requested  by the user.

params


This function implements the MCMC algorithm derived in order to draw samples of
the posterior distribution of the interest parameters in generalized elliptical semi-parametric models,
which combines Gibbs sampler and Metropolis-Hastings algorithm.


Set of tools to perform the statistical inference based on the Bayesian approach for regression models under the assumption that independent additive errors follow normal, Student-t, slash, contaminated normal, Laplace or symmetric hyperbolic distributions, i.e., additive errors follow a scale mixtures of normal distributions. The regression models considered in this package are: (i) Generalized elliptical semi-parametric models, where both location and dispersion parameters of the response variable distribution include non-parametric additive components described by using B-splines; and (ii) Flexible measurement error models under the presence of homoscedastic and heteroscedastic random errors, which admit explanatory variables with and without measurement additive errors as well as the presence of a non-parametric components approximated by using B-splines.

mcmc.gesm: MCMC algorithm for Generalized Elliptical Semi-parametric Models

Description

Usage

Arguments