| my.IC {pencopulaCond} | R Documentation |
Calculating the AIC-value
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(\lambda)= - 2*l({\bf u},\hat{\bf{b}}) + 2*df(\lambda)
cAIC is calculated as
cAIC(\lambda)= - 2*l({\bf u},\hat{\bf{b}}) + 2*df(\lambda) + \frac{2df(\lambda)(df(\lambda)+1)}{n-df(\lambda)-1}
BIC is calculated as
BIC(\lambda)= 2*l({\bf u},\hat{\bf{b}}) + 2*df(\lambda)*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 <cschellhase@wiwi.uni-bielefeld.de>
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.