bw_tt_pi: Nearest-neighbor bandwidth selection for the tapered...
In tnagler/kdecopula: Kernel Smoothing for Bivariate Copula Densities
Description
Usage
Arguments
Value
Author(s)
References
Description
The smoothing parameters are selected by the method of Wen and Wu (2015).
Usage
1
2
3
Arguments
udata
data.
rho.add
logical; whether a rotation (correlation) parameter shall be
included.
Value
Optimal smoothing parameters as in Wen and Wu (2015): a numeric
vector of length 4; entries are (h, ρ, θ_1, θ_2).
Author(s)
Kuangyu Wen
References
Wen, K. and Wu, X. (2015).
Transformation-Kernel Estimation of the Copula Density,
Working paper,
http://agecon2.tamu.edu/people/faculty/wu-ximing/agecon2/public/copula.pdf
tnagler/kdecopula documentation built on May 31, 2019, 4:43 p.m.
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Description Usage Arguments Value Author(s) References
Description
The smoothing parameters are selected by the method of Wen and Wu (2015).
Usage
1 2 3 |
Arguments
udata |
data. |
rho.add |
logical; whether a rotation (correlation) parameter shall be included. |
Value
Optimal smoothing parameters as in Wen and Wu (2015): a numeric vector of length 4; entries are (h, ρ, θ_1, θ_2).
Author(s)
Kuangyu Wen
References
Wen, K. and Wu, X. (2015). Transformation-Kernel Estimation of the Copula Density, Working paper, http://agecon2.tamu.edu/people/faculty/wu-ximing/agecon2/public/copula.pdf
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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