Inference on function-valued parameters using a restricted score test

• 成果类型: Article
• 署名作者: Hudson, Aaron; Carone, Marco; Shojaie, Ali
• 署名单位: Fred Hutchinson Cancer Center; University of Washington; University of Washington Seattle
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412; 1467-9868
• DOI: 10.1093/jrsssb/qkag043
• 发表日期: 2026-09
• 页码: 1321-1344
• 关键词: nonparametric testing; non-pathwise differentiability; score test; simultaneous confidence bands; confidence bands; intervals

摘要

It is often of interest to make inference on an unknown function that is a local parameter of the data-generating mechanism, such as a density or regression function. Such estimands can typically only be estimated at a slower-than-parametric rate in nonparametric and semiparametric models, and performing calibrated inference can be challenging. In many cases, these estimands can be expressed as the minimizer of a population risk functional. Here, we propose a general framework that leverages such representation and provides a nonparametric extension of the score test for inference on an infinite-dimensional risk minimizer. We demonstrate that our framework is applicable in a wide variety of problems. As both analytic and computational examples, we describe how to use our general approach for inference on a mean regression function under (i) nonparametric and (ii) partially additive models, and evaluate the operating characteristics of the resulting procedures via simulations. Assessment of effect heterogeneity, inference on density functions, and conditional independence testing are discussed as additional examples.