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Composite quantile regression for the single-index model

Author

Listed:
  • Fan, Yan
  • Härdle, Wolfgang Karl
  • Wang, Weining
  • Zhu, Lixing

Abstract

Quantile regression is in the focus of many estimation techniques and is an important tool in data analysis. When it comes to nonparametric specifications of the conditional quantile (or more generally tail) curve one faces, as in mean regression, a dimensionality problem. We propose a projection based single index model specification. For very high dimensional regressors X one faces yet another dimensionality problem and needs to balance precision vs. dimension. Such a balance may be achieved by combining semiparametric ideas with variable selection techniques.

Suggested Citation

  • Fan, Yan & Härdle, Wolfgang Karl & Wang, Weining & Zhu, Lixing, 2013. "Composite quantile regression for the single-index model," SFB 649 Discussion Papers 2013-010, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    • Handle: RePEc:zbw:sfb649:sfb649dp2013-010
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    Citations

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    Cited by:

    1. Yazhao Lv & Riquan Zhang & Weihua Zhao & Jicai Liu, 2015. "Quantile regression and variable selection of partial linear single-index model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(2), pages 375-409, April.
    2. repec:hum:wpaper:sfb649dp2017-003 is not listed on IDEAS
    3. Kangning Wang & Lu Lin, 2017. "Robust and efficient direction identification for groupwise additive multiple-index models and its applications," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(1), pages 22-45, March.
    4. Jing Sun, 2016. "Composite quantile regression for single-index models with asymmetric errors," Computational Statistics, Springer, vol. 31(1), pages 329-351, March.
    5. Poeschel, Friedrich, 2012. "Assortative matching through signals," VfS Annual Conference 2012 (Goettingen): New Approaches and Challenges for the Labor Market of the 21st Century 62061, Verein für Socialpolitik / German Economic Association.
    6. Yu, Lining & Härdle, Wolfgang Karl & Borke, Lukas & Benschop, Thijs, 2017. "FRM: A financial risk meter based on penalizing tail events occurrence," SFB 649 Discussion Papers 2017-003, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    7. Lining Yu & Wolfgang Karl Hardle & Lukas Borke & Thijs Benschop, 2020. "An AI approach to measuring financial risk," Papers 2009.13222, arXiv.org.

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