Article ID Journal Published Year Pages File Type
1152216 Statistics & Probability Letters 2012 8 Pages PDF
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
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