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GACV for quantile smoothing splines


  • Yuan, Ming


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  • Yuan, Ming, 2006. "GACV for quantile smoothing splines," Computational Statistics & Data Analysis, Elsevier, vol. 50(3), pages 813-829, February.
  • Handle: RePEc:eee:csdana:v:50:y:2006:i:3:p:813-829

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    References listed on IDEAS

    1. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731, March.
    2. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
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    Cited by:

    1. Shim, Jooyong & Hwang, Changha, 2009. "Support vector censored quantile regression under random censoring," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 912-919, February.
    2. Sungwan Bang & Soo-Heang Eo & Yong Mee Cho & Myoungshic Jhun & HyungJun Cho, 2016. "Non-crossing weighted kernel quantile regression with right censored data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(1), pages 100-121, January.
    3. Shih-Kang Chao & Wolfgang Karl Härdle & Hien Pham-Thu, 2014. "Credit Risk Calibration based on CDS Spreads," SFB 649 Discussion Papers SFB649DP2014-026, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    4. 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.
    5. repec:spr:aistmt:v:69:y:2017:i:4:d:10.1007_s10463-016-0566-9 is not listed on IDEAS
    6. Songfeng Zheng, 2014. "A generalized Newton algorithm for quantile regression models," Computational Statistics, Springer, vol. 29(6), pages 1403-1426, December.
    7. Jooyong Shim & Changha Hwang & Kyungha Seok, 2016. "Support vector quantile regression with varying coefficients," Computational Statistics, Springer, vol. 31(3), pages 1015-1030, September.
    8. Huang, Lele & Zhao, Junlong & Wang, Huiwen & Wang, Siyang, 2016. "Robust shrinkage estimation and selection for functional multiple linear model through LAD loss," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 384-400.
    9. Jooyong Shim & Changha Hwang & Kyungha Seok, 2014. "Composite support vector quantile regression estimation," Computational Statistics, Springer, vol. 29(6), pages 1651-1665, December.
    10. Schnabel, Sabine K. & Eilers, Paul H.C., 2009. "Optimal expectile smoothing," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4168-4177, October.
    11. Lenka Zbonakova & Wolfgang Karl Härdle & Weining Wang, 2016. "Time Varying Quantile Lasso," SFB 649 Discussion Papers SFB649DP2016-047, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    12. Bang, Sungwan & Jhun, Myoungshic, 2012. "Simultaneous estimation and factor selection in quantile regression via adaptive sup-norm regularization," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 813-826.
    13. Lian, Heng & Meng, Jie & Fan, Zengyan, 2015. "Simultaneous estimation of linear conditional quantiles with penalized splines," Journal of Multivariate Analysis, Elsevier, vol. 141(C), pages 1-21.
    14. repec:eee:stapro:v:126:y:2017:i:c:p:205-211 is not listed on IDEAS
    15. Wu, Chaojiang & Yu, Yan, 2014. "Partially linear modeling of conditional quantiles using penalized splines," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 170-187.
    16. Jooyong Shim & Yongtae Kim & Jangtaek Lee & Changha Hwang, 2012. "Estimating value at risk with semiparametric support vector quantile regression," Computational Statistics, Springer, vol. 27(4), pages 685-700, December.
    17. Qifa Xu & Cuixia Jiang & Yaoyao He, 2016. "An exponentially weighted quantile regression via SVM with application to estimating multiperiod VaR," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(2), pages 285-320, June.

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