Inference on function-valued parameters using a restricted score test

TL;DR

This paper introduces a general nonparametric score test framework for inference on unknown function-valued parameters, such as density or regression functions, in complex models where estimators converge slowly.

Contribution

It develops a novel, broadly applicable score test extension for infinite-dimensional risk minimizers in nonparametric and semiparametric models.

Findings

Framework applicable to various problems

Effective in inference on regression functions

Validated through simulations

Abstract

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.