my.IC: Calculating the AIC- and BIC-value

In pencopula: Flexible Copula Density Estimation with Penalized Hierarchical B-Splines

Description

Calculating the AIC- and BIC- value of the copula density estimation. Therefore, we add the unpenalized log likelihood of the estimation and the degree of freedom, which are

Usage

my.IC(penden.env)

Arguments

penden.env

Containing all information, environment of pencopula()

Details

AIC is calculated as AIC(λ)= - 2*l({\bf u},\hat{\bf{b}}) + 2*df(λ)

BIC is calculated as BIC(λ)= 2*l({\bf u},\hat{\bf{b}}) + 2*df(λ)*log(n)

Value

AIC	sum of twice the negative non-penalized log likelihood and mytrace
trace	calculated mytrace as the sum of the diagonal matrix df, which results as the product of the inverse of the penalized second order derivative of the log likelihood with the non-penalized second order derivative of the log likelihood
BIC	sum of twice the non-penalized log likelihood and log(n)

All values are saved in the environment.

Author(s)

Christian Schellhase <[e-mail]>

References

Flexible Copula Density Estimation with Penalized Hierarchical B-Splines, Kauermann G., Schellhase C. and Ruppert, D. (2013), Scandinavian Journal of Statistics 40(4), 685-705.

pencopula documentation built on May 2, 2019, 7:21 a.m.