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A convergent algorithm for quantile regression with smoothing splines

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  • Bosch, Ronald J.
  • Ye, Yinyu
  • Woodworth, George G.

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  • Bosch, Ronald J. & Ye, Yinyu & Woodworth, George G., 1995. "A convergent algorithm for quantile regression with smoothing splines," Computational Statistics & Data Analysis, Elsevier, vol. 19(6), pages 613-630, June.
    • Handle: RePEc:eee:csdana:v:19:y:1995:i:6:p:613-630
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    References listed on IDEAS

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    1. Roger W. Koenker & Vasco D'Orey, 1987. "Computing Regression Quantiles," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(3), pages 383-393, November.
    2. Cunningham, J. K. & Eubank, R. L. & Hsing, T., 1991. "M-type smoothing splines with auxiliary scale estimation," Computational Statistics & Data Analysis, Elsevier, vol. 11(1), pages 43-51, January.
    3. Antoch, J. & Janssen, P., 1989. "Nonparametric regression M-quantiles," Statistics & Probability Letters, Elsevier, vol. 8(4), pages 355-362, September.
    4. Michael J. Todd & Yinyu Ye, 1988. "A Centered Projective Algorithm for Linear Programming," Cowles Foundation Discussion Papers 861, Cowles Foundation for Research in Economics, Yale University.
    5. Prochazka, Bohumir, 1988. "Regression quantiles and trimmed least squares estimator in the nonlinear regression model," Computational Statistics & Data Analysis, Elsevier, vol. 6(4), pages 385-391, June.
    6. Michael J. Todd & Yinyu Ye, 1990. "A Centered Projective Algorithm for Linear Programming," Mathematics of Operations Research, INFORMS, vol. 15(3), pages 508-529, August.
    7. T. J. Cole, 1988. "Fitting Smoothed Centile Curves to Reference Data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 151(3), pages 385-406, May.
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    Cited by:

    1. DeRossi, G. & Harvey, A., 2006. "Time-Varying Quantiles," Cambridge Working Papers in Economics 0649, Faculty of Economics, University of Cambridge.
    2. De Rossi, Giuliano & Harvey, Andrew, 2009. "Quantiles, expectiles and splines," Journal of Econometrics, Elsevier, vol. 152(2), pages 179-185, October.
    3. Rodrigues, T. & Dortet-Bernadet, J.-L. & Fan, Y., 2019. "Simultaneous fitting of Bayesian penalised quantile splines," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 93-109.
    4. Laurini, Márcio P., 2007. "Imposing No-Arbitrage Conditions In Implied Volatility Surfaces Using Constrained Smoothing Splines," Insper Working Papers wpe_89, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    5. Reiss Philip T. & Huang Lei, 2012. "Smoothness Selection for Penalized Quantile Regression Splines," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-27, May.
    6. Marcio Laurini, 2007. "A note on the use of quantile regression in beta convergence analysis," Economics Bulletin, AccessEcon, vol. 3(52), pages 1-8.
    7. Zhang, Likun & Castillo, Enrique del & Berglund, Andrew J. & Tingley, Martin P. & Govind, Nirmal, 2020. "Computing confidence intervals from massive data via penalized quantile smoothing splines," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    8. Poletti Laurini, Márcio & Moura, Marcelo, 2010. "Constrained smoothing B-splines for the term structure of interest rates," Insurance: Mathematics and Economics, Elsevier, vol. 46(2), pages 339-350, April.

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