Computational Statistics

, Volume 33, Issue 2, pp 659–674 | Cite as

Robust empirical likelihood for partially linear models via weighted composite quantile regression

  • Peixin Zhao
  • Xiaoshuang Zhou
Original Paper


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.


Weighted composite quantile regression Empirical likelihood Partially linear model QR decomposition 



This research is supported by the Chongqing Research Program of Basic Theory and Advanced Technology (cstc2016jcyjA0151), the Social Science Planning Project of Chongqing (2015PY24), the Fifth Batch of Excellent Talent Support Program for Chongqing Colleges and University (2017-35-16), and the Scientific Research Foundation of Chongqing Technology and Business University (2015-56-06).


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Mathematics and StatisticsChongqing Technology and Business UniversityChongqingChina
  2. 2.School of Mathematical SciencesDezhou UniversityDezhouChina

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