NONPARAMETRIC REGRESSION UNDER DOUBLE-SAMPLING DESIGNS

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 ApplicationsSCI(E)EI中国科学引文数据库(CSCD)0ARTICLE1167-1752