**AUTHORS:**Mingxing Zhang, Zixin Liu

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**ABSTRACT:**
This paper investigates a class of estimation problems of the semiparametric model with missing data.
In order to overcome the robust defect of traditional complete data estimation method and regression imputation
estimation technique, we propose a modified imputation estimation approach called Kriging-regression imputation.
Compared with previous method used in the references cited therein , the new proposed method not only makes
more use of the data information, but also has better robustness. Model estimation and asymptotic distribution
of the estimators are also derived theoretically. In order to improve the robustness, LASSO technique is further
introduced into Kriging-regression imputation. Numerical experiment is also provided to show the effectiveness
and superiority of our method.

**KEYWORDS:**
semiparameter model, data missing, imputation techniques, asymptotic normality, consistency

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