A new minimum contrast approach for inference in single-index models

JOURNAL OF MULTIVARIATE ANALYSIS

卷:158

Article

Li, Weiyu^1,2  Patilea, Valentin¹ 

[1] CREST Ensai, Paris, France

[2] Shandong Univ, Jinan, Peoples R China

关键词: Conditional law;    Kernel smoothing;    Semiparametric regression;    Single-index assumption;    U-statistics;   

DOI  :  10.1016/j.jmva.2017.03.009

来源: Elsevier

【 摘 要 】

Semiparametric single-index models represent an appealing compromise between parametric and nonparametric approaches and have been widely investigated in the literature. The underlying assumption in single-index models is that the information carried by the vector of covariates could be summarized by a one-dimensional projection. We propose a new, general inference approach for such models, based on a quadratic form criterion involving kernel smoothing. The approach could be applied with general single index assumptions, in particular for mean regression models and conditional law models. The covariates could be unbounded and no trimming is necessary. A resampling method for building confidence intervals for the index parameter is proposed. Our empirical experiments reveal that the new method performs well in practice. (C) 2017 Elsevier Inc. All rights reserved.

【 授权许可】

Free