Robust nonparametric estimation with missing data |
| |
Authors: | Graciela Boente Wenceslao González–Manteiga Ana Pérez–González |
| |
Institution: | 1. Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires and CONICET, Ciudad Universitaria, Pabellón 2, Buenos Aires C1428EHA, Argentina;2. Universidad de Santiago de Compostela, Spain;3. Universidad de Vigo, Spain |
| |
Abstract: | In this paper, under a nonparametric regression model, we introduce two families of robust procedures to estimate the regression function when missing data occur in the response. The first proposal is based on a local M-functional applied to the conditional distribution function estimate adapted to the presence of missing data. The second proposal imputes the missing responses using the local M-smoother based on the observed sample and then estimates the regression function with the completed sample. We show that the robust procedures considered are consistent and asymptotically normally distributed. A robust procedure to select the smoothing parameter is also discussed. |
| |
Keywords: | Asymptotic properties Kernel weights Missing data Nonparametric regression Robust estimation |
本文献已被 ScienceDirect 等数据库收录! |