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Estimation in partially linear models with missing responses at random

By Qihua Wang and Zhihua Sun

Abstract

A partially linear model is considered when the responses are missing at random. Imputation, semiparametric regression surrogate and inverse marginal probability weighted approaches are developed to estimate the regression coefficients and the nonparametric function, respectively. All the proposed estimators for the regression coefficients are shown to be asymptotically normal, and the estimators for the nonparametric function are proved to converge at an optimal rate. A simulation study is conducted to compare the finite sample behavior of the proposed estimators.Imputation estimator Regression surrogate estimator Inverse marginal probability weighted estimator Asymptotic normality

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