aicbs {bs}R Documentation

Akaike information criterium (AIC) for a sample of the BSD

Description

The function aicbs() gives the AIC value assuming an BSD with parameters alpha and beta. aicbs() is based on the invariance property of the MLE.

Usage

aicbs(x)

Arguments

x Vector of observations.

Details

The SIC is a selection model criterion based on information loss. According to this criterion, it is possible to choose 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). AIC is an alternative information criterium.

Value

The function aicbs() gives the AIC value.

Author(s)

Víctor Leiva <victor.leiva@uv.cl>, Hugo Hernández <hugo.hernande@msn.com>, and Marco Riquelme <mriquelm@ucm.cl>.

References

Schwarz, S. (1978). Estimating the dimension of the model. Annals of Statistics, 6, 461-464.

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

Leiva, V., Hernández, H., and Riquelme, M. (2006). A New Package for the Birnbaum-Saunders Distribution. Rnews, 6/4, 35-40. (http://www.r-project.org)

Examples

## Load data sets
data(psi31)

## Calculus of AIC for psi31
aicbs(psi31)

[Package bs version 1.0 Index]