sicigt {ig}R Documentation

Schwartz information criterion for a sample from the IGTD

Description

The function sicig() gives the Schwartz information criterion (SIC) value assuming an IGTD with parameters mu, lambda and a specific kernel.

Usage

sicigt(x, nu = 1.0, kernel = "normal")

Arguments

x Vector of observations.
nu Shape parameter corresponding to the degrees of freedom of the t distribution. In the case of the Laplace, logistic, normal kernels, nu can be fixed at the value 1.0 since this parameter is not involved in these kernels.
kernel Kernel of the pdf of the associated symmetrical distribution by means of which the IGTD is obtained. The kernels: "laplace", "logistic", "normal" and "t" are available.

Details

The SIC is a selection model criterion based on information loss. According to this criterion, it is possible to choice a hypothetic model that better describe the data set considering the smaller SIC value. The SIC is defined as SIC = -l(theta)/n+ p log(n)/(2n), where l(theta) is the log-likelihood function associated with the model, n is the sample size and p is the number of involved parameters; for more details see Spieglhaiter, Best, Carlin and van der Linde (2002).

Value

sicigt() gives the value for the SIC of the IGTD.

Author(s)

Víctor Leiva <victor.leiva@uv.cl; victor.leiva@yahoo.com>,
Hugo Hernández <hugo.hernandez.p@gmail.com> and
Antonio Sanhueza <asanhueza@ufro.cl>.

References

Spieglhaiter, D. J., Best, N. G., Carlin, B. P., van der Linde, A. (2002). Bayesian measures of complexity and fit. Journal of the Royal Statistical Society Series B 64, 1-34.

Sanhueza, A., Leiva, V., Balakrishnan, N. (2008). A new class of inverse Gaussian type distributions. Metrika (in press).

Examples

## Generates a sample from the IGTD with normal kernel
x <- rigt(300, mu = 1.0, lambda = 1.0, kernel = "normal")

## Computes the SIC value of the IGTD with normal kernel from the data x
sicigt(x, 1.0, kernel = "normal")

[Package ig version 1.2 Index]