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Solution path for quantile regression with epsilon-insensitive loss in a reproducing kernel Hilbert space

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  • Park, Jinho

Abstract

This article proposes an algorithm for finding the solution paths of estimated quantile functions as the regularization parameter varies. The solution paths constitute a useful tool for choosing the optimal regularization parameter.

Suggested Citation

  • Park, Jinho, 2017. "Solution path for quantile regression with epsilon-insensitive loss in a reproducing kernel Hilbert space," Statistics & Probability Letters, Elsevier, vol. 126(C), pages 205-211.
    • Handle: RePEc:eee:stapro:v:126:y:2017:i:c:p:205-211
    DOI: 10.1016/j.spl.2017.03.006
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    References listed on IDEAS

    as
    1. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731, September.
    2. Li, Youjuan & Liu, Yufeng & Zhu, Ji, 2007. "Quantile Regression in Reproducing Kernel Hilbert Spaces," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 255-268, March.
    3. Yuan, Ming, 2006. "GACV for quantile smoothing splines," Computational Statistics & Data Analysis, Elsevier, vol. 50(3), pages 813-829, February.
    4. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    5. Park, Jinho & Kim, Jeankyung, 2011. "Quantile regression with an epsilon-insensitive loss in a reproducing kernel Hilbert space," Statistics & Probability Letters, Elsevier, vol. 81(1), pages 62-70, January.
    6. Jinho Park, 2015. "Quantile Regression with Left-Truncated and Right-Censored Data in a Reproducing Kernel Hilbert Space," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(7), pages 1523-1536, April.
    Full references (including those not matched with items on IDEAS)

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