Backfitting tests in generalized structured models

• 成果类型: Article
• 署名作者: Mammen, E.; Sperlich, S.
• 署名单位: Ruprecht Karls University Heidelberg; University of Geneva
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asaa108
• 发表日期: 2022
• 页码: 137152
• 关键词: Nonparametric regression; ADDITIVE-MODELS; TRANSFORMATION; inference

摘要

We introduce bootstrap tests for semiparametric generalized structured models. These can be used for testing different kinds of model specifications like separability, functional forms and homogeneity of effects, or for performing variable selection in a large class of semiparametric models. The test statistics are based on the comparison of non- and semiparametric alternatives in which both the null hypothesis and the alternative are non- or semiparametric. All estimators are obtained by smooth backfitting. Simulation studies show excellent performance of the test procedures.