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	NONPARAMETRIC REGRESSION UNDER DOUBLE-SAMPLING DESIGNS
	Jiang, Xuejun ; Jiang, Jiancheng ; Liu, Yanling
	2011
关键词	Local linear smoother surrogate validation sample OUTCOME DATA NONRESPONSE INFERENCE SUBJECT
英文摘要	This paper studies nonparametric estimation of the regression function with surrogate outcome data under double-sampling designs, where a proxy response is observed for the full sample and the true response is observed on a validation set. A new estimation approach is proposed for estimating the regression function. The authors first estimate the regression function with a kernel smoother based on the validation subsample, and then improve the estimation by utilizing the information on the incomplete observations from the non-validation subsample and the surrogate of response from the full sample. Asymptotic normality of the proposed estimator is derived. The effectiveness of the proposed method is demonstrated via simulations.; Mathematics, Interdisciplinary Applications; SCI(E); EI; 中国科学引文数据库(CSCD); 0; ARTICLE; 1; 167-175; 24
语种	英语
出处	SCI ; EI
出版者	journal of systems science complexity
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/157630]
专题	数学科学学院
推荐引用方式 GB/T 7714	Jiang, Xuejun,Jiang, Jiancheng,Liu, Yanling. NONPARAMETRIC REGRESSION UNDER DOUBLE-SAMPLING DESIGNS. 2011-01-01.

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