hqbs {bs} | R Documentation |
The function hqbs()
gives the HQC value assuming an BSD with parameters alpha and beta.
hqbs()
is based on the invariance property of the MLE.
hqbs(x)
x |
Vector of observations. |
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). HQC is a alternative information criterium.
The function hqbs()
gives the AIC value.
Víctor Leiva <victor.leiva@uv.cl>, Hugo Hernández <hugo.hernande@msn.com>, and Marco Riquelme <mriquelm@ucm.cl>.
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)
## Load data sets data(psi31) ## Calculus of HQC for psi31 hqbs(psi31)