Economics at your fingertips  

Empirical likelihood for heteroscedastic partially linear single-index models with growing dimensional data

Jianglin Fang (), Wanrong Liu and Xuewen Lu
Additional contact information
Jianglin Fang: Hunan Institute of Engineering
Wanrong Liu: Hunan Normal University
Xuewen Lu: University of Calgary

Metrika: International Journal for Theoretical and Applied Statistics, 2018, vol. 81, issue 3, 255-281

Abstract: Abstract In this paper, we propose a new approach to the empirical likelihood inference for the parameters in heteroscedastic partially linear single-index models. In the growing dimensional setting, it is proved that estimators based on semiparametric efficient score have the asymptotic consistency, and the limit distribution of the empirical log-likelihood ratio statistic for parameters $$(\beta ^{\top },\theta ^{\top })^{\top }$$ ( β ⊤ , θ ⊤ ) ⊤ is a normal distribution. Furthermore, we show that the empirical log-likelihood ratio based on the subvector of $$\beta $$ β is an asymptotic chi-square random variable, which can be used to construct the confidence interval or region for the subvector of $$\beta $$ β . The proposed method can naturally be applied to deal with pure single-index models and partially linear models with high-dimensional data. The performance of the proposed method is illustrated via a real data application and numerical simulations.

Keywords: Empirical likelihood; High-dimensional data; Heteroscedasticity; Partially linear single-index model; Semiparametric efficiency (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link) Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from

Access Statistics for this article

Metrika: International Journal for Theoretical and Applied Statistics is currently edited by U. Kamps and Norbert Henze

More articles in Metrika: International Journal for Theoretical and Applied Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla ().

Page updated 2019-04-09
Handle: RePEc:spr:metrik:v:81:y:2018:i:3:d:10.1007_s00184-018-0642-7