| knots.start {pencopulaCond} | R Documentation |
Calculating the knots.
Description
Calculating the equidistant knots for the estimation. Moreover, transformation of the knots are possible.
Usage
knots.start(penden.env)
knots.transform(d,alpha = 0, symmetric = TRUE)
knots.order(penden.env)
Arguments
penden.env |
Containing all information, environment of pencopula() |
d |
Hierarchy level of the marginal hierarchical B-spline basis. |
alpha |
Default = 0. Alpha is a tuning parameter, shifting the knots. |
symmetric |
Default = TRUE. If FALSE, the knots are selected without symmetry. |
Details
'Knots.order' sorts the knots in the order, in which they disappear in the hierarchical B-spline basis.
Value
knots |
Selected and sorted marginal knots for the estimation. |
knots.help |
Extended set of knots. It is needed for calculating the distribution function, help points for the integration of the B-spline density basis. |
k.order |
Order of the knots, corresponding to their order in the hierarchical B-spline density basis. |
knots.t |
The knots ordered with 'k.order' for further fucntions. |
tilde.Psi.knots.d |
Hierarchical B-Spline density basis for 'knots'. |
tilde.Psi.knots.d.help |
Hierarchical B-Spline density basis for 'knots.help'. |
All values are saved in the environment.
Author(s)
Christian Schellhase <cschellhase@wiwi.uni-bielefeld.de>
References
Flexible Copula Density Estimation with Penalized Hierarchical B-Splines, Kauermann G., Schellhase C. and Ruppert, D. (2013), Scandinavian Journal of Statistics 40(4), 685-705.
Estimating Non-Simplified Vine Copulas Using Penalized Splines, Schellhase, C. and Spanhel, F. (2017), Statistics and Computing.