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	Robust local polynomial regression for dependent data
	Jiang, JC ; Mack, YP
	2001
关键词	data-driven local M-estimator local polynomial regression mixing condition one-step robustness NONPARAMETRIC REGRESSION ASYMPTOTIC-DISTRIBUTION MIXING PROCESSES ADDITIVE-MODELS ESTIMATORS SMOOTHERS
英文摘要	Let (X-j, Y-j)(j=1)(n) be a realization of a bivariate jointly strictly stationary process. We consider a robust estimator of the regression function M(x) = E(Y/X = x) by using local polynomial regression techniques. The estimator is a local M-estimator weighted by a kernel function. Under mixing conditions satisfied by many time series models, together with other appropriate conditions, consistency and asymptotic normality results are established. One-step local M-estimators are introduced to reduce computational burden. In addition, we give a data-driven choice for minimizing the scale factor involving the Psi -function in the asymptotic covariance expression, by drawing a parallel with the class of Huber's Psi -functions. The method is illustrated via two examples.; Statistics & Probability; SCI(E); 23; ARTICLE; 3; 705-722; 11
语种	英语
出处	SCI
出版者	statistica sinica
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/401881]
专题	数学科学学院
推荐引用方式 GB/T 7714	Jiang, JC,Mack, YP. Robust local polynomial regression for dependent data. 2001-01-01.

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