| Type: | Package |
| Title: | Bagging Bandwidth Selection in Kernel Density and Regression Estimation |
| Version: | 1.1 |
| Date: | 2024-07-22 |
| Description: | Bagging bandwidth selection methods for the Parzen-Rosenblatt and Nadaraya-Watson estimators. These bandwidth selectors can achieve greater statistical precision than their non-bagged counterparts while being computationally fast. See Barreiro-Ures et al. (2020) <doi:10.1093/biomet/asaa092> and Barreiro-Ures et al. (2021) <doi:10.48550/arXiv.2105.04134>. |
| License: | GPL-3 |
| URL: | https://rubenfcasal.github.io/baggingbwsel/, https://github.com/rubenfcasal/baggingbwsel/ |
| BugReports: | https://github.com/rubenfcasal/baggingbwsel/issues/ |
| Encoding: | UTF-8 |
| Depends: | mclust, foreach |
| Imports: | Rcpp (≥ 1.0.3), parallel, doParallel, kedd, stats, sm, nor1mix, misc3d |
| Suggests: | rgl, tkrplot, rpanel |
| LinkingTo: | Rcpp |
| RoxygenNote: | 7.3.2 |
| NeedsCompilation: | yes |
| Packaged: | 2024-07-22 21:00:44 UTC; ruben |
| Author: | Daniel Barreiro-Ures [aut], Ruben Fernandez-Casal [aut, cre], Jeffrey Hart [aut], Ricardo Cao [aut], Mario Francisco-Fernandez [aut] |
| Maintainer: | Ruben Fernandez-Casal <rubenfcasal@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2024-07-27 16:20:09 UTC |
baggingbwsel: Bagging bandwidth selection in kernel density and regression estimation
Description
This package implements bagging bandwidth selection methods for the Parzen-Rosenblatt kernel density estimator, and for the Nadaraya-Watson and local polynomial kernel regression estimators. These bandwidth selectors can achieve greater statistical precision than their non-bagged counterparts while being computationally fast. See Barreiro-Ures et al. (2021a) and Barreiro-Ures et al. (2021b).
Author(s)
Maintainer: Ruben Fernandez-Casal rubenfcasal@gmail.com
Authors:
Daniel Barreiro-Ures daniel.barreiro.ures@udc.es
Jeffrey Hart
Ricardo Cao
Mario Francisco-Fernandez
References
Barreiro-Ures, D., Cao, R., Francisco-Fernández, M., & Hart, J. D. (2021a). Bagging cross-validated bandwidths with application to big data. Biometrika, 108(4), 981-988, doi:10.1093/biomet/asaa092.
Barreiro-Ures, D., Cao, R., & Francisco-Fernández, M. (2021b). Bagging cross-validated bandwidth selection in nonparametric regression estimation with applications to large-sized samples. arXiv preprint, doi:10.48550/arXiv.2105.04134.
See Also
Useful links:
Report bugs at https://github.com/rubenfcasal/baggingbwsel/issues/
Bagged CV bandwidth selector for Parzen-Rosenblatt estimator
Description
Bagged CV bandwidth selector for Parzen-Rosenblatt estimator
Usage
bagcv(x, r, s, h0, h1, nb = r, ncores = parallel::detectCores())
Arguments
x |
Vector. Sample. |
r |
Positive integer. Size of the subsamples. |
s |
Positive integer. Number of subsamples. |
h0 |
Positive real number. Range over which to minimize, left bound. |
h1 |
Positive real number. Range over which to minimize, right bound. |
nb |
Positive integer. Number of bins. |
ncores |
Positive integer. Number of cores with which to parallelize the computations. |
Details
Bagged cross-validation bandwidth selector for the Parzen-Rosenblatt estimator.
Value
Bagged CV bandwidth.
Examples
set.seed(1)
x <- rnorm(10^6)
bagcv(x, 5000, 100, 0.01, 1, 1000, 2)
Bagged CV bandwidth selector for local polynomial kernel regression.
Description
Bagged CV bandwidth selector for local polynomial kernel regression.
Usage
bagreg(
x,
y,
r,
s,
h0,
h1,
nb = r,
ncores = parallel::detectCores(),
poly.index = 0
)
Arguments
x |
Covariate vector. |
y |
Response vector. |
r |
Positive integer. Size of the subsamples. |
s |
Positive integer. Number of subsamples. |
h0 |
Positive real number. Range over which to minimize, left bound. |
h1 |
Positive real number. Range over which to minimize, right bound. |
nb |
Positive integer. Number of bins to use in cross-validation. |
ncores |
Positive integer. Number of cores with which to parallelize the computations. |
poly.index |
Non-negative integer defining local constant (0) or local linear (1) smoothing. Default value: 0 (Nadaraya-Watson estimator). |
Details
Bagged cross-validation bandwidth selector for local polynomial kernel regression.
Value
Bagged CV bandwidth.
Examples
set.seed(1)
x <- rnorm(10^5)
y <- 2*x+rnorm(1e5,0,0.5)
bagreg(x, y, 1000, 10, 0.01, 1, 1000, 2)
Bagging bootstrap bandwidth selector for Parzen-Rosenblatt estimator
Description
Bagging bootstrap bandwidth selector for Parzen-Rosenblatt estimator
Usage
hboot_bag(
x,
m = n,
N = 1,
nb = 1000L,
g,
lower,
upper,
ncores = parallel::detectCores(logical = FALSE)
)
Arguments
x |
Vector. Sample. |
m |
Positive integer. Size of the subsamples. |
N |
Positive integer. Number of subsamples. |
nb |
Positive integer. Number of bins. |
g |
Positive real number. Pilot bandwidth. |
lower |
Positive real number. Range over which to minimize, left bound. |
upper |
Positive real number. Range over which to minimize, right bound. |
ncores |
Positive integer. Number of cores with which to parallelize the computations. |
Details
Bagging bootstrap bandwidth selector for the Parzen-Rosenblatt estimator.
Value
Bagged CV bandwidth.
Examples
set.seed(1)
x <- rnorm(10^5)
hboot_bag(x, 5000, 10, 1000, lower=0.001, upper=1, ncores=2)
Generalized bagging CV bandwidth selector for Parzen-Rosenblatt estimator
Description
Generalized bagging CV bandwidth selector for Parzen-Rosenblatt estimator
Usage
hsss_dens(x, r, s, nb = r, h0, h1, ncores = parallel::detectCores())
Arguments
x |
Vector. Sample. |
r |
Positive integer. Size of the subsamples. |
s |
Positive integer. Number of subsamples. |
nb |
Positive integer. Number of bins. |
h0 |
Positive real number. Range over which to minimize, left bound. |
h1 |
Positive real number. Range over which to minimize, right bound. |
ncores |
Positive integer. Number of cores with which to parallelize the computations. |
Details
Generalized bagging cross-validation bandwidth selector for the Parzen-Rosenblatt estimator.
Value
Bagged CV bandwidth.
Examples
set.seed(1)
x <- rnorm(10^5)
hsss_dens(x, 5000, 100, 1000, 0.001, 1, 2)
Estimation of the optimal subsample size for bagged CV bandwidth for Parzen-Rosenblatt estimator
Description
Estimation of the optimal subsample size for bagged CV bandwidth for Parzen-Rosenblatt estimator
Usage
mopt(x, N, r = 1000, s = 100, ncores = parallel::detectCores())
Arguments
x |
Vector. Sample. |
N |
Positive integer. Number of subsamples for the bagged bandwidth. |
r |
Positive integer. Size of the subsamples. |
s |
Positive integer. Number of subsamples. |
ncores |
Positive integer. Number of cores with which to parallelize the computations. |
Details
Estimates the optimal size of the subsamples for the bagged CV bandwidth selector for the Parzen-Rosenblatt estimator.
Value
Estimate of the optimal subsample size.
Examples
set.seed(1)
x <- rt(10^5, 5)
mopt(x, 500, 500, 10, 2)
Second order bagging CV bandwidth selector for Parzen-Rosenblatt estimator
Description
Second order bagging CV bandwidth selector for Parzen-Rosenblatt estimator
Usage
tss_dens(x, r, s, h0, h1, nb = 1000, ncores = 1)
Arguments
x |
Vector. Sample. |
r |
Vector. The two subsample sizes. |
s |
Positive integer. Number of subsamples. |
h0 |
Positive real number. Range over which to minimize, left bound. |
h1 |
Positive real number. Range over which to minimize, right bound. |
nb |
Positive integer. Number of bins. |
ncores |
Positive integer. Number of cores with which to parallelize the computations. |
Details
Second order bagging cross-validation bandwidth selector for the Parzen-Rosenblatt estimator.
Value
Second order bagging CV bandwidth.
Examples
set.seed(1)
x <- rnorm(10^5)
tss_dens(x, 5000, 10, 0.01, 1, 1000, 2)