Hypotheses tests in boundary regression models

TL;DR

This paper develops uniform convergence rates for boundary regression models with one-sided errors, enabling new goodness-of-fit and independence tests, especially for irregular error distributions, with theoretical and simulation validation.

Contribution

It introduces a novel estimation approach for boundary regression functions and derives convergence rates that facilitate asymptotic distribution-free hypothesis testing.

Findings

Faster convergence rates for irregular error distributions near endpoints.

Asymptotic √n-equivalence of residual-based and true error distribution functions.

Development of distribution-free tests for error independence and boundary monotonicity.

Abstract

Consider a nonparametric regression model with one-sided errors and regression function in a general H\"older class. We estimate the regression function via minimization of the local integral of a polynomial approximation. We show uniform rates of convergence for the simple regression estimator as well as for a smooth version. These rates carry over to mean regression models with a symmetric and bounded error distribution. In such a setting, one obtains faster rates for irregular error distributions concentrating sufficient mass near the endpoints than for the usual regular distributions. The results are applied to prove asymptotic $\sqrt{n}$-equivalence of a residual-based (sequential) empirical distribution function to the (sequential) empirical distribution function of unobserved errors in the case of irregular error distributions. This result is remarkably different from corresponding results in mean regression with regular errors. It can readily be applied to develop goodness-of-fit tests for the error distribution. We present some examples and investigate the small sample performance in a simulation study. We further discuss asymptotically distribution-free hypotheses tests for independence of the error distribution from the points of measurement and for monotonicity of the boundary function as well.