Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1152216 | Statistics & Probability Letters | 2012 | 8 Pages |
Abstract
We consider how to incorporate auxiliary information to improve quantile regression via empirical likelihood. We propose a novel framework and show that our approach yields more efficient estimates compared to those from the conventional quantile regression. The efficiency gain is quantified theoretically and demonstrated empirically via simulation studies.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Cheng Yong Tang, Chenlei Leng,