# my.IC: Calculating the AIC-, cAIC- and BIC-value In penDvine: Flexible Pair-Copula Estimation in D-Vines 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

 1 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 <cschellhase@wiwi.uni-bielefeld.de>

## References

Flexible Pair-Copula Estimation in D-vines using Bivariate Penalized Splines, Kauermann G. and Schellhase C. (2014+), Statistics and Computing (to appear).

penDvine documentation built on May 2, 2019, 1:06 p.m.