Local Linear Additive Quantile Regression
Keming Yu and
Zudi Lu
Scandinavian Journal of Statistics, 2004, vol. 31, issue 3, 333-346
Abstract:
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.
Date: 2004
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https://doi.org/10.1111/j.1467-9469.2004.03_035.x
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Persistent link: https://EconPapers.repec.org/RePEc:bla:scjsta:v:31:y:2004:i:3:p:333-346
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