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      Efficient quantile regression for heteroscedastic models

      Jung, Yoonsuh; Lee, Yoonkyung; MacEachern, Steve N,
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      HeteroQR_8_24.pdf
      Accepted version, 373.8Kb
      DOI
       10.1080/00949655.2014.967244
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      Jung, 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
      2015
      Type
      Journal Article
      Rights
      This is an author’s accepted version of an article published in the journal: Journal of Statistical Computation and Simulation. © 2015 Taylor & Francis.
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      • Computing and Mathematical Sciences Papers [1455]
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