Description Usage Arguments Value Author(s) References
Calculating penalized (conditional) copula density with penalized hierarchical B-splines
1 2 3 |
data |
'data' contains the data. 'data' has to be a matrix or a data.frame. The number of columns of 'data' is p. |
d |
refers to the hierachy level of the marginal hierarchical B-spline, default is d=3. |
D |
referes to the maximum hierachy level, default is D=3. If D<d, it follows D<-d. |
q |
degree of the marginal hierarchical B-spline. |
base |
By default, the used marginal basis is a 'B-spline'. Second possible option is 'Bernstein', using a Bernstein polynomial basis. |
max.iter |
maximum number of iteration, the default is max.iter=20. |
test.ind |
Default=FALSE. If test.ind=TRUE, the fitted log-likelihood of each pair-copula is evaluated. If ("log.like"/"n"<0.001), where "n" is the sample size, the program set the corresponding pair copula as independence copula. We do not use this in our simulations or applications in the article. |
lambda |
p-dimensional vector of penalty parameters, the values can be different. Default is lambda=c(100,100). |
pen.order |
The order of differences for the penalization, default is pen.order=2. |
data.frame |
reference to the data. Default reference is the parent.frame(). |
cond |
Determining if a conditional copula is estimated. Default=FALSE, only suitable for p=3. |
fix.lambda |
Default=FALSE, using the algorithm in the paper for estimating the optimal penalty parameter. If fix.lambda=TRUE, lambda is constant throughout the estimation. |
id |
Optional, one set id to any value. Especially important for simulations, starting with several starting values for lambda. |
Returning an object of class pencopula. The class pencopula consists of the environment 'penden.env', which includes all calculated values of the estimation approach. For a fast overview of the main results, one can use the function 'print.pencopula()'.
Christian Schellhase <cschellhase@wiwi.uni-bielefeld.de>
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.
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