pen.log.like: Calculating the log likelihood

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

Description

Calculating the considered log likelihood.

Usage

pen.log.like(penden.env, cal=FALSE, temp=FALSE)

Arguments

penden.env	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.

Details

The calculation depends on the estimated weights v, the penalized splines Phi and the penalty paramters lambda.

\eqn{l(v,lambda)=sum(log(Φ(u_i)v))-0.5*b^T \tilde{P} v}

The needed values are saved in the environment.

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.

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.