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Local Linear Additive Quantile Regression

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  • Keming Yu
  • Zudi Lu

Abstract

. We consider non‐parametric additive quantile regression estimation by kernel‐weighted local linear fitting. The estimator is based on localizing the characterization of quantile regression as the minimizer of the appropriate ‘check function’. A backfitting algorithm and a heuristic rule for selecting the smoothing parameter are explored. We also study the estimation of average‐derivative quantile regression under the additive model. The techniques are illustrated by a simulated example and a real data set.

Suggested Citation

  • Keming Yu & Zudi Lu, 2004. "Local Linear Additive Quantile Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 31(3), pages 333-346, September.
    • Handle: RePEc:bla:scjsta:v:31:y:2004:i:3:p:333-346
    DOI: 10.1111/j.1467-9469.2004.03_035.x
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