ADAPTIVE FUNCTION ESTIMATION IN NONPARAMETRIC REGRESSION WITH ONE-SIDED ERRORS

• 成果类型: Article
• 署名作者: Jirak, Moritz; Meister, Alexander; Reiss, Markus
• 署名单位: Humboldt University of Berlin; University of Rostock
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/14-AOS1248
• 发表日期: 2014
• 页码: 1970-2002
• 关键词: maximum-likelihood; efficient estimation; frontier estimation; hull estimators; dea estimators; Minimax; CONVERGENCE; parameter; inference; monotone

摘要

We consider the model of nonregular nonparametric regression where smoothness constraints are imposed on the regression function f and the regression errors are assumed to decay with some sharpness level at their endpoints. The aim of this paper is to construct an adaptive estimator for the regression function f. In contrast to the standard model where local averaging is fruitful, the nonregular conditions require a substantial different treatment based on local extreme values. We study this model under the realistic setting in which both the smoothness degree beta > 0 and the sharpness degree a is an element of (0, infinity) are unknown in advance. We construct adaptation procedures applying a nested version of Lepski's method and the negative Hill estimator which show no loss in the convergence rates with respect to the general L-q-risk and a logarithmic loss with respect to the pointwise risk. Optimality of these rates is proved for a is an element of (0, infinity). Some numerical simulations and an application to real data are provided.