bw_tll_nn: Nearest-neighbor bandwidth selection for the transformation...

In kdecopula: Kernel Smoothing for Bivariate Copula Densities

Description

The smoothing parameters is selected by the method of Geenens et al. (2017). It uses principal components for the rotation matrix and selects the nearest neighbor fraction along each principal direction by approximate least-squares cross-validation.

Usage

bw_tll_nn(udata, deg)

Arguments

udata	data.
deg	degree of the polynomial.

Value

A list with entries:

B

rotation matrix,

alpha

nearest neighbor fraction (this one is multiplied with mult in kdecop()),

kappa

correction factor for differences in roughness along the axes,

see Geenens et al. (2017).

References

Geenens, G., Charpentier, A., and Paindaveine, D. (2017). Probit transformation for nonparametric kernel estimation of the copula density. Bernoulli, 23(3), 1848-1873.

kdecopula documentation built on May 2, 2019, 1:06 a.m.