Nonparametric regression under double-sampling designs |
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Authors: | Xuejun Jiang Jiancheng Jiang Yanling Liu |
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Institution: | 1.School of Mathematical Sciences,Peking University,Beijing,China;2.School of Statistics and Mathematics,Zhongnan University of Economics and Law,Wuhan,China;3.Department of Mathematics and Statistics,University of North Carolina,Charlotte,USA |
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Abstract: | 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. |
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Keywords: | |
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