Study Of Missing Data Imputation Based On Semi-parametric Model

Data missing is a common phenomenon in solving practical problems. Missing data imputation is an important issue in data analysis. Missing values may generate bias and affect the quality of the statistics, so adopting some methods to remedy missing data is essential. Missing data imputation is an important method, which is commonly used to deal with missing data. This paper focused on studying the method.Parametric model, nonparametric model and semi-parametric model are the common models we used for missing data imputation. Considering the advantages of semi-parametric model, this paper try to discuss the imputation method based on semi-parametric model with the advantages both in parametric and non-parametric model. Firstly, this paper estimated semi-parametric model through using the least squares kernel estimator. Then covariate vectors were used to estimate the target variable in order to establish a data set that can be used for imputation. In empirical part, this paper compared several common methods, including single mean imputation, stratified mean imputation, the ratio imputation, closest distance imputation, stratified hot deck imputation, regression imputation and imputation based on semi-parametric model. The comparison results show that the imputation based on semi-parametric model can make a better evaluation about distribution function and imputed MSE.