NONPARAMETRIC REGRESSION UNDER DOUBLE-SAMPLING DESIGNS  

作　　者：Xuejun JIANG Jiancheng JIANG Yanling LIU 

机构地区：[1]School of Mathematical Sciences, Peking University, Beijing 100871, China School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan 430073,China [2]Department of Mathematics and Statistics, University of North Carolina, Charlotte, North Carolina 28223, USA [3]School of Mathematical Sciences, Peking University, Beijing 100871, China

出　　处：《Journal of Systems Science & Complexity》2011年第1期167-175,共9页系统科学与复杂性学报（英文版）

基　　金：This research is supported by the National Natural Science Foundation of the US under Grant No. DMS- 0906482.

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.

关 键 词：Local linear smoother surrogate validation sample 

分 类 号：O212.2[理学—概率论与数理统计] O212.7[理学—数学]