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February 2019 Estimation and hypotheses testing in boundary regression models
Holger Drees, Natalie Neumeyer, Leonie Selk
Bernoulli 25(1): 424-463 (February 2019). DOI: 10.3150/17-BEJ992

Abstract

Consider a nonparametric regression model with one-sided errors and regression function in a general Hölder 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.

Citation

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Holger Drees. Natalie Neumeyer. Leonie Selk. "Estimation and hypotheses testing in boundary regression models." Bernoulli 25 (1) 424 - 463, February 2019. https://doi.org/10.3150/17-BEJ992

Information

Received: 1 September 2016; Revised: 1 June 2017; Published: February 2019
First available in Project Euclid: 12 December 2018

zbMATH: 07007213
MathSciNet: MR3892325
Digital Object Identifier: 10.3150/17-BEJ992

Rights: Copyright © 2019 Bernoulli Society for Mathematical Statistics and Probability

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Vol.25 • No. 1 • February 2019
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