sicgbs {gbs} | R Documentation |
The function sicgbs()
gives the Schwartz information criterion (SIC)
value assuming a GBSD with parameters α, βand a
specific kernel.
sicgbs(x, nu = 1.0, kernel = "normal")
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 GBSD is obtained. The kernels: {"laplace"}, {"logistic"}, {"normal"} and {"t"} are available. |
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 et al. (2002).
sicgbs()
gives the value for the SIC of the GBSD.
Barros, Michelli <michelli.karinne@gmail.com>
Leiva, Victor <victor.leiva@uv.cl, victor.leiva@yahoo.com>
Paula, Gilberto A. <giapaula@ime.usp.br>
Diaz-Garcia, J.A., Leiva, V. (2005) A new family of life distributions based on elliptically contoured distributions. J. Stat. Plan. Infer. 128:445-457 (Erratum: J. Stat. Plan. Infer. 137:1512-1513).
Leiva, V., Barros, M., Paula, G.A., Sanhueza, A. (2008) Generalized Birnbaum-Saunders distributions applied to air pollutant concentration. Environmetrics 19:235-249.
Sanhueza, A., Leiva, V., Balakrishnan, N. (2008) The generalized Birnbaum-Saunders distribution and its theory, methodology and application. Comm. Stat. Theory and Meth. 37:645-670.
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.
## Generates a sample from the GBSD with normal kernel x <- rgbs(300, alpha = 1.0, beta = 1.0, nu = 1.0, kernel = "normal") ## Computes the SIC value of the GBSD with normal kernel from the data x sicgbs(x, nu = 1.0, kernel = "normal")