Quantile smoothing splines

Roger Koenker, Pin Ng, Stephen Portnoy

Research output: Contribution to journalArticlepeer-review

431 Scopus citations


SUMMARY: Although nonparametric regression has traditionally focused on the estimation of conditional mean functions, nonparametric estimation of conditional quantile functions is often of substantial practical interest. We explore a class of quantile smoothing splines, defined as solutions to minσ Pc(yi_g{(xi)}+λ (int10lgn(x)/pdx)1/p with pt(u)=u{t_I(u< )}, pages; 1, and appropriately chosen G. For the particular choices p = 1 and p = ∞ we characterise solutions g as splines, and discuss computation by standard l1-type linear programming techniques. At λ =0, g interpolates the τ th quantiles at the distinct design points, and for λ sufficiently large g is the linear regression quantile fit (Koenker & Bassett, 1978) to the observations. Because the methods estimate conditional quantile functions they possess an inherent robustness to extreme observations in the yi's. The entire path of solutions, in the quantile parameter τ, or the penalty parameter λ2, may be efficiently computed by parametric linear programming methods. We note that the approach may be easily adapted to impose monotonicity and/or convexity constraints on the fitted function. An example is provided to illustrate the use of the proposed methods.

Original languageEnglish (US)
Pages (from-to)673-680
Number of pages8
Issue number4
StatePublished - Dec 1994


  • Bandwidth selection
  • Nonparametric regression
  • Quantile
  • Smoothing
  • Spline

ASJC Scopus subject areas

  • Statistics and Probability
  • General Mathematics
  • Agricultural and Biological Sciences (miscellaneous)
  • General Agricultural and Biological Sciences
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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