【摘 要】
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Local polynomial regression is widely used for nonparametric regression.However,the efficiency of least squares (LS) based methods is adversely affected by
【机 构】
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LPMCandDepartmentofStatistics,SchoolofMathematicalSciences,NankaiUniversity
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Local polynomial regression is widely used for nonparametric regression.However,the efficiency of least squares (LS) based methods is adversely affected by outlying observations and heavy tailed distributions.On the other hand,the least absolute deviation (LAD) estimator is more robust,but may be ine±cient for many distributions of interest.Kai,Li and Zou (2010) propose a nonparametric regression technique called local composite quantile regression (LCQR) smoothing to improve local polynomial regression further.However,the performance of LCQR depends on the choice of the number of quantiles to combine,a meta parameter which plays vital roles in balancing the performance of LS and LAD based methods.
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