Efficient quantile regression for heteroscedastic models
Files
Accepted version, 373.8Kb
Citation
Export citationJung, Y., Lee, Y., & MacEachern, S. N. (2015). Efficient quantile regression for heteroscedastic models. Journal of Statistical Computation and Simulation, 85(13), 2548–2568. http://doi.org/10.1080/00949655.2014.967244
Permanent Research Commons link: https://hdl.handle.net/10289/9355
Abstract
Quantile regression (QR) provides estimates of a range of conditional quantiles. This stands in contrast to traditional regression techniques, which focus on a single conditional mean function. Lee et al. [Regularization of case-specific parameters for robustness and efficiency. Statist Sci. 2012;27(3):350–372] proposed efficient QR by rounding the sharp corner of the loss. The main modification generally involves an asymmetric ℓ₂ adjustment of the loss function around zero. We extend the idea of ℓ₂ adjusted QR to linear heterogeneous models. The ℓ₂ adjustment is constructed to diminish as sample size grows. Conditions to retain consistency properties are also provided.
Date
2015Type
Rights
This is an author’s accepted version of an article published in the journal: Journal of Statistical Computation and Simulation. © 2015 Taylor & Francis.