kdevinecop: Kernel estimation of vine copula densities

In kdevine: Multivariate Kernel Density Estimation with Vine Copulas

Kernel estimation of vine copula densities

Description

The function estimates a vine copula density using kernel estimators for the pair copulas (based on the kdecopula package).

Usage

kdevinecop(
  data,
  matrix = NA,
  method = "TLL2",
  renorm.iter = 3L,
  mult = 1,
  test.level = NA,
  trunc.level = NA,
  treecrit = "tau",
  cores = 1,
  info = FALSE
)

Arguments

data	(n x d) matrix of copula data (have to lie in [0,1^d]).
matrix	R-Vine matrix (n x d) specifying the structure of the vine; if NA (default) the structure selection heuristic of Dissman et al. (2013) is applied.
method	see kdecop.
renorm.iter	see kdecop.
mult	see kdecop.
test.level	significance level for independence test. If you provide a number in [0, 1], an independence test (BiCopIndTest) will be performed for each pair; if the null hypothesis of independence cannot be rejected, the independence copula will be set for this pair. If test.level = NA (default), no independence test will be performed.
trunc.level	integer; the truncation level. All pair copulas in trees above the truncation level will be set to independence.
treecrit	criterion for structure selection; defaults to "tau".
cores	integer; if cores > 1, estimation will be parallized within each tree (using foreach).
info	logical; if TRUE, additional information about the estimate will be gathered (see kdecop).

Value

An object of class kdevinecop. That is, a list containing

T1, T2, ...	lists of the estimted pair copulas in each tree,
matrix	the structure matrix of the vine,
info	additional information about the fit (if info = TRUE).

References

Nagler, T., Czado, C. (2016)
Evading the curse of dimensionality in nonparametric density estimation with simplified vine copulas.
Journal of Multivariate Analysis 151, 69-89 (doi:10.1016/j.jmva.2016.07.003)

Nagler, T., Schellhase, C. and Czado, C. (2017)
Nonparametric estimation of simplified vine copula models: comparison of methods arXiv:1701.00845

Dissmann, J., Brechmann, E. C., Czado, C., and Kurowicka, D. (2013).
Selecting and estimating regular vine copulae and application to financial returns.
Computational Statistics & Data Analysis, 59(0):52–69.

See Also

dkdevinecop, kdecop, BiCopIndTest, foreach

Examples

data(wdbc, package = "kdecopula")
# rank-transform to copula data (margins are uniform)
u <- VineCopula::pobs(wdbc[, 5:7], ties = "average")

fit <- kdevinecop(u)                   # estimate density
dkdevinecop(c(0.1, 0.1, 0.1), fit)     # evaluate density estimate
contour(fit)                           # contour matrix (Gaussian scale)
pairs(rkdevinecop(500, fit))           # plot simulated data

kdevine documentation built on Oct. 18, 2022, 5:05 p.m.