Bagging Bandwidth Selection in Kernel Density and Regression Estimation

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) and Barreiro-Ures et al. (2021) .

baggingbwsel: Bagging bandwidth selection in kernel density and regression estimation

Version 1.1

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).

Installation

baggingbwsel is not yet available from CRAN, but you can install the development version from github with:

# install.packages("remotes")
remotes::install_github("rubenfcasal/baggingbwsel")

Note also that, as this package requires compilation, Windows users need to have previously installed the appropriate version of Rtools, and OS X users need to have installed Xcode.

Authors

• Daniel Barreiro-Ures ([email protected])

• Ruben Fernandez-Casal ([email protected])

• Jeffrey Hart

• Ricardo Cao

• Mario Francisco-Fernandez

Maintainer: Ruben Fernandez-Casal (Dep. Mathematics, University of A Coruña, Spain). Please send comments, error reports or suggestions to [email protected].

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, .

• 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.