Estimation in partially linear models with missing responses at random

Authors:
Qihua Wang;Zhihua Sun
Affiliations:
Academy of Mathematics and Systems Science, Chinese Academy of Science, Beijing 100080, P.R. China and Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, H ...;Academy of Mathematics and Systems Science, Chinese Academy of Science, Beijing 100080, P.R. China
Venue:
Journal of Multivariate Analysis
Year:
2007

Citing 1
Cited 3

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Empirical likelihood for semiparametric regression model with missing response data

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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.