Local Linear Additive Quantile Regression |
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Authors: | Keming Yu Zudi Lu |
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Institution: | University of Plymouth;and Academy of Mathematics and System Sciences, Chinese Academy of Sciences |
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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. |
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Keywords: | additive models average derivative backfitting algorithm bandwidth selection local linear fitting quantile regression |
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