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

In penRvine: Flexible R-Vines Estimation Using Bivariate Penalized Splines

Description

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

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, cAIC and BIC are calculated.

Details

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

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

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

Value

AIC	sum of twice the negative non-penalized log likelihood and mytrace
cAIC	corrected AIC.
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 Pair-Copula Estimation in D-vines using Bivariate Penalized Splines, Kauermann, G. and Schellhase, C. (2014), Statistics and Computing 24(6): 1081-1100).

Nonparametric estimation of simplified vines: comparison of methods, Nagler N., Schellhase, C. and Czado, C. (2017) Dependence Modeling.

penRvine documentation built on May 30, 2017, 2:20 a.m.