bw_t: Bandwidth selection for the transformation kernel estimator
In kdecopula: Kernel Smoothing for Bivariate Copula Densities
Description
Usage
Arguments
Details
Value
References
Description
The bandwidth is selected by a rule of thumb. It approximately minimizes
the MISE of the Gaussian copula on the transformed domain. The usual normal
reference matrix is multiplied by 1.25 to account for the higher variance
on the copula level.
Usage
1
bw_t(udata)
Arguments
udata
data.
Details
The formula is
1.25 n^{-1 / 6} \hat{Σ}^{1/2},
where \hat{Sigma} is empirical covariance matrix of the transformed
random vector.
Value
A 2 x 2 bandwidth matrix.
References
Nagler, T. (2014).
Kernel Methods for Vine Copula Estimation.
Master's Thesis, Technische Universitaet Muenchen,
https://mediatum.ub.tum.de/node?id=1231221
kdecopula documentation built on May 2, 2019, 1:06 a.m.
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Description Usage Arguments Details Value References
Description
The bandwidth is selected by a rule of thumb. It approximately minimizes the MISE of the Gaussian copula on the transformed domain. The usual normal reference matrix is multiplied by 1.25 to account for the higher variance on the copula level.
Usage
1 | bw_t(udata)
|
Arguments
udata |
data. |
Details
The formula is
1.25 n^{-1 / 6} \hat{Σ}^{1/2},
where \hat{Sigma} is empirical covariance matrix of the transformed random vector.
Value
A 2 x 2 bandwidth matrix.
References
Nagler, T. (2014). Kernel Methods for Vine Copula Estimation. Master's Thesis, Technische Universitaet Muenchen, https://mediatum.ub.tum.de/node?id=1231221
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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