Empirical-likelihood-based inference for parameters defined by the general estimating equa-tions of Qin and Lawless(1994)remains an active research topic.When the response is missing at random(MAR)and the dimension of covariate is not low,the authors propose a two-stage estimation procedure by using the dimension-reduced kernel estimators in conjunction with an unbiased estimating function based on augmented inverse probability weighting and multiple imputation(AIPW-MI)meth-ods.The authors show that the resulting estimator achieves consistency and asymptotic normality.In addition,the corresponding empirical likelihood ratio statistics asymptotically follow central chi-square distributions when evaluated at the true parameter.The finite-sample performance of the proposed estimator is studied through simulation,and an application to HIV-CD4 data set is also presented.