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In this article, we consider an imputation method to handle missing responsevalues based on semiparametric quantile regression estimation. In the proposedmethod, the missing response values are generated using the estimatedconditional quantile regression function at given values of covariates. Weadopt the generalized method of moments for estimation of parameters definedthrough a general estimation equation. We demonstrate that the proposedestimator, which combines both semiparametric quantile regression imputationand generalized method of moments, has competitive edge against some of themost widely used parametric and non-parametric imputation estimators. Theconsistency and the asymptotic normality of our estimator are established andvariance estimation is provided. Results from a limited simulation study and anempirical study are presented to show the adequacy of the proposed method.

Generalized Method of Moments Estimator Based On Semiparametric Quantile Regression Imputation