The R Journal: article published in 2021, volume 13:1

Package wsbackfit for Smooth Backfitting Estimation of Generalized Structured Models
Javier Roca-Pardiñas, María Xosé Rodríguez-Álvarez and Stefan Sperlich , The R Journal (2021) 13:1, pages 330-350.

Abstract 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

Received: 2020-06-03; online 2021-06-07, supplementary material, (3 KiB)
CRAN packages: sBF, wsbackfit, BayesX, gam, mgcv, GAMLSS, GAMBoost, np
CRAN Task Views implied by cited CRAN packages: Econometrics, SocialSciences, Bayesian, Environmetrics

This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

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