my.IC: Calculating the AIC-value

In pencopulaCond: Estimating Non-Simplified Vine Copulas Using Penalized Splines

Description

Calculating the AIC-value and cAIC-value of the copula density estimation.

Usage

my.IC(penden.env,temp=FALSE)

Arguments

penden.env	Containing all information, environment of paircopula()
temp	Default=FALSE, if TRUE temporary values of AIC and cAIC are calculated.

Details

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

cAIC is calculated as cAIC(λ)= - 2*l({\bf u},\hat{\bf{b}}) + 2*df(λ) + \frac{2df(λ)(df(λ)+1)}{n-df(λ)-1}

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 df(lambda)
cAIC	sum of twice the negative non-penalized log likelihood and df(lambda) and (2df(lambda)(df(lambda)+1))/(n-df(lambda)-1)
BIC	sum of twice the non-penalized log likelihood and log(n)*df(lambda)

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.

Estimating Non-Simplified Vine Copulas Using Penalized Splines, Schellhase, C. and Spanhel, F. (2017), Statistics and Computing.

pencopulaCond documentation built on May 1, 2019, 7:56 p.m.