Package wsbackfit for Smooth Backfitting Estimation of Generalized Structured Models

A package is introduced that provides the weighted smooth backfitting estimator for a large family of popular semiparametric regression models. This family is known as generalized structured models, comprising, for example, generalized varying coefficient model, generalized additive models, mixtures, potentially including parametric parts. The kernel-based weighted smooth backfitting belongs to the statistically most efficient procedures for this model class. Its asymptotic properties are well-understood thanks to the large body of literature about this estimator. The introduced weights allow for the inclusion of sampling weights, trimming, and efficient estimation under heteroscedasticity. Further options facilitate easy handling of aggregated data, prediction, and the presentation of estimation results. Cross-validation methods are provided which can be used for model and bandwidth selection.1

Javier Roca-Pardiñas , María Xosé Rodríguez-Álvarez , Stefan Sperlich
2020-06-03

CRAN packages used

sBF, wsbackfit, BayesX, gam, mgcv, GAMLSS, GAMBoost, np

CRAN Task Views implied by cited packages

Econometrics, SocialSciences, Bayesian, Environmetrics

Reuse

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Citation

For attribution, please cite this work as

Roca-Pardiñas, et al., "The R Journal: Package wsbackfit for Smooth Backfitting Estimation of Generalized Structured Models", {The R Journal}, 2020

BibTeX citation

@article{RJ-2021-042,
  author = {Roca-Pardiñas, Javier and Rodríguez-Álvarez, María Xosé and Sperlich, Stefan},
  title = {The R Journal: Package wsbackfit for Smooth Backfitting Estimation of Generalized Structured Models},
  journal = {{The R Journal}},
  year = {2020},
  note = {https://doi.org/10.32614/RJ-2021-042},
  doi = {10.32614/RJ-2021-042},
  volume = {13},
  issue = {1},
  issn = {2073-4859},
  pages = {330-350}
}