Derv2: Calculating the second order derivative of the paircopula...
In penRvine: Flexible R-Vines Estimation Using Bivariate Penalized Splines

Description Usage Arguments Details Value Author(s) References

Calculating the second order derivative of the paircopula likelihood function w.r.t. parameter v.

1	Derv2(penden.env,temp=FALSE,lambda=NULL,lam.fit=FALSE)

`penden.env`	Containing all information, environment of paircopula().
`temp`	Default=FALSE,if TRUE temporary calculations of optimal parameters are done.
`lambda`	Default=NULL, i.e. the saved smoothing parameter lambda in the environment is used. Alternatively, temporary values of lambda are used for optimization of lambda.
`lam.fit`	Default=FALSE, indicating if the first derivative is calculated to determine the next optimal penalty parameter lambda.

We approximate the second order derivative in this approach with the negative fisher information.

`Derv2.pen`	second order derivative w.r.t. v with penalty
`Derv2.cal`	second order derivative w.r.t. v without penalty. Needed for calculating of e.g. AIC.

Derv2.cal and Derv2.pen are saved in the environment.

Christian Schellhase <cschellhase@wiwi.uni-bielefeld.de>

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.