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Quantile regression: applications and current research areas

Keming Yu et al.

Journal of the Royal Statistical Society, Series D (The Statistician)2003https://doi.org/10.1111/1467-9884.00363article
ABDC A
Weight
0.74

Abstract

Summary. Quantile regression offers a more complete statistical model than mean regression and now has widespread applications. Consequently, we provide a review of this technique. We begin with an introduction to and motivation for quantile regression. We then discuss some typical application areas. Next we outline various approaches to estimation. We finish by briefly summarizing some recent research areas.

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https://doi.org/https://doi.org/10.1111/1467-9884.00363

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@article{keming2003,
  title        = {{Quantile regression: applications and current research areas}},
  author       = {Keming Yu et al.},
  journal      = {Journal of the Royal Statistical Society, Series D (The Statistician)},
  year         = {2003},
  doi          = {https://doi.org/https://doi.org/10.1111/1467-9884.00363},
}

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Evidence weight

0.74

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.99 × 0.4 = 0.39
M · momentum0.79 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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