bw_tt_pi: Nearest-neighbor bandwidth selection for the tapered...
In kdecopula: Kernel Smoothing for Bivariate Copula Densities
bw_tt_pi R Documentation
Nearest-neighbor bandwidth selection for the tapered transformation estimator
Description
The smoothing parameters are selected by the method of Wen and Wu (2015).
Usage
bw_tt_pi(udata, rho.add = TRUE)
bw_tt_cv(udata, rho.add = T)
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, \rho, \theta_1, \theta_2).
Author(s)
Kuangyu Wen
References
Wen, K. and Wu, X. (2018).
Transformation-Kernel Estimation of Copula Densities
Journal of Business & Economic Statistics, 38(1), 148–164.
kdecopula documentation built on April 4, 2025, 2:28 a.m.
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| bw_tt_pi | R Documentation |
Nearest-neighbor bandwidth selection for the tapered transformation estimator
Description
The smoothing parameters are selected by the method of Wen and Wu (2015).
Usage
bw_tt_pi(udata, rho.add = TRUE)
bw_tt_cv(udata, rho.add = T)
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, \rho, \theta_1, \theta_2).
Author(s)
Kuangyu Wen
References
Wen, K. and Wu, X. (2018). Transformation-Kernel Estimation of Copula Densities Journal of Business & Economic Statistics, 38(1), 148–164.
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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