文章基本信息

标题：Package wsbackfit for Smooth Backfitting Estimation of Generalized Structured Models
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作者：Javier Roca-Pardiñas ; María Xosé Rodríguez-Álvarez ; Stefan Sperlich 等
期刊名称：R News
印刷版ISSN：1609-3631
出版年度：2021
卷号：13
期号：1
页码：330-350
语种：English
出版社：The R Foundation for Statistical Computing
摘要：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.