Containing all information, environment of paircopula()
cal
if TRUE, the final weights of one iteration are used for
the calculation of the penalized log likelihood.
temp
if TRUE, the iteration for optimal weights is still in
progress and the temporary weights are used for calculation.
Value
pen.log.like
Penalized log likelihood of the paircopula density.
log.like
Log-Likelihood of the paircopula density.
The values are saved in the environment.
Details
The calculation depends on the estimated weights v, the penalized
splines Phi and the penalty paramters lambda.
$$l(v,\lambda)=\sum_{i=1}^{n} \left[ \log \{\sum_{i=1}^n
\boldsymbol\Phi(u_i)\} v\right]- \frac 12 v^T \boldsymbol{P}(\lambda) b$$
The needed values are saved in the environment.
References
Flexible Pair-Copula Estimation in D-vines using Bivariate Penalized
Splines, Kauermann G. and Schellhase C. (2014+), Statistics and Computing (to appear).