Journal
SCANDINAVIAN JOURNAL OF STATISTICS
Volume 31, Issue 3, Pages 333-346Publisher
WILEY
DOI: 10.1111/j.1467-9469.2004.03_035.x
Keywords
additive models; average derivative; backfitting algorithm; bandwidth selection; local linear fitting; quantile regression
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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.
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