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Local Linear Additive Quantile Regression
Author(s) -
Yu Keming,
Lu Zudi
Publication year - 2004
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/j.1467-9469.2004.03_035.x
Subject(s) - mathematics , quantile regression , quantile , estimator , additive model , kernel regression , nonparametric regression , statistics , kernel smoother , smoothing , linear regression , kernel (algebra) , polynomial regression , kernel method , artificial intelligence , computer science , combinatorics , radial basis function kernel , support vector machine
. 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.