lam.search: Search optimal starting vlaue for lambda

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

Description

Starts the estimation of the paircopula with different starting values of the penalty parameter 'lambda'.

Usage

lam.search(data,K,lam,m,max.iter,q,base,fix.lambda,pen,id)

Arguments

data	'data' contains the data. 'data' has to be a matrix or a data.frame with two columns.
K	K is the degree of the Bernstein polynomials. In the case of linear B-spline basis functions, K+1 nodes are used for the basis functions.
lam	Vector of potential starting values for lambda
m	Indicating the order of differences to be penalised. Default is "m=2".
max.iter	maximum number of iteration, the default is max.iter=51.
q	Order of B-spline basis, i.e. default q=2 means linear B-spline basis.
base	Type of basis function.
fix.lambda	Indicator if fix lambda is used.
pen	Indicating type of penalisation, corresponding to basis type 'basis'.
id	Indification number

Details

Fitting of pair-copulas using each potential starting value for the penalty parameter 'lambda'. Returning the pair-copula with lowest cAIC in the fitted list.

Author(s)

Christian Schellhase <[e-mail]>

References

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.