glpost {Bayesiantreg}

Posterior value of the degrees of freedom

Description

Calculate a value for posterior density of the degrees of freedom parameter

Usage

glpost(y, x, z, betas.ini, gammas.ini, gl.ini, Maxi, lambda, p, prior, type)

Arguments

y

object of class matrix, with the dependent variables.

x

object of class matrix, with the variables for modelling the mean.

z

object of class matrix, with the variables for modelling the variance.

betas.ini

a vector with the proposal beta parameters.

gammas.ini

a vector with the proposal gamma parameters.

gl.ini

a vector with the proposal degrees of freedom parameter.

Maxi

a number indicating the maximum value for the uniform prior an the uniforme proposal

lambda

a number indicating the mean parameter value for the Poisson prior an the Poisson proposal

p

a number indicating the parameter value for the Jeffrey's prior an the Jeffrey's proposal

type

a vector that can take the value "D" if the prior for the degrees of freedom considered as discrete or "C" if it is continuous.

prior

when type is "D", it is a vector that can take the values of "poi" for a Poisson prior or "unif" for a uniform prior. When type is "C", it is a vector that can take the values of "exp" for the exponential prior, "unif" for the uniform prior or "J2" for the Jeffrey's prior.

Details

Generate the posterior density for the degrees of freedom proposed by Marin and Cepeda (_).

Value

value

a value with the posterior denity for the degrees of freedom

Author(s)

Margarita Marin [e-mail], Edilberto Cepeda-Cuervo [e-mail]

References

1. Marin and Cepeda-Cuervo (_). A Bayesian regression model for the non-standardized t distribution with location, scale and degrees of freedom parameters. Unpublished

2. Cepeda-Cuervo E. (2001). Modelagem da variabilidade em modelos lineares generalizados. Unpublished Ph.D. tesis. Instituto de Matematicas. Universidade Federal do Rio do Janeiro.

3. Cepeda C., E. and Gamerman D. (2001). Bayesian Modeling of Variance Heterogeneity in Normal Regression Models. Brazilian Journal of Probability and Statistics. 14, 207-221