Derv1: Calculating the first derivative of the paircopula likelihood...

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

Description

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

Usage

Derv1(penden.env,temp=FALSE,lambda=NULL,lam.fit=FALSE)

Arguments

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.

Details

The calculation of the first derivative of the paircopula likelihood function w.r.t. b equals

\eqn{s(v,lambda)=}

with

\eqn{P(lambda)}

is the penalty matrix, saved in the environment.

Value

Derv1.pen

first order derivation of the penalized likelihood function w.r.t. parameter v.

Derv1.pen is 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.