Robust empirical likelihood for partially linear models via weighted composite quantile regression
Peixin Zhao () and
Additional contact information
Peixin Zhao: Chongqing Technology and Business University
Xiaoshuang Zhou: Dezhou University
Computational Statistics, 2018, vol. 33, issue 2, No 5, 659-674
Abstract In this paper, we investigate robust empirical likelihood inferences for partially linear models. Based on weighted composite quantile regression and QR decomposition technology, we propose a new estimation method for the parametric components. Under some regularity conditions, we prove that the proposed empirical log-likelihood ratio is asymptotically chi-squared, and then the confidence intervals for the parametric components are constructed. The resulting estimators for parametric components are not affected by the nonparametric components, and then it is more robust, and is easy for application in practice. Some simulations analysis and a real data application are conducted for further illustrating the performance of the proposed method.
Keywords: Weighted composite quantile regression; Empirical likelihood; Partially linear model; QR decomposition (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
http://link.springer.com/10.1007/s00180-018-0793-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:33:y:2018:i:2:d:10.1007_s00180-018-0793-z
Ordering information: This journal article can be ordered from
Access Statistics for this article
Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik
More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla ().