Comparison of Methods to Handle Missing Values in Linear Models with Missing Data
Yongsong Qin · Guangxi kexue · 2009
When the response variable is missing at random in a linear model,three means are considered to handle missing values.They are deleting cases with missing values,deterministic and fractional linear regression imputations.Based on these methods,three estimators are studied for the regression parameters such as the mean,the distribution functions and the quantiles of the response variable.Simulations using statistical R software are conducted to compare the performances of three estimators.The results show that if we use the methods except for the deterministic imputation,the values of SE decrease and the estimations are more accurate as the sample sizes increase.We can also see that the values of SEs increase and the estimatiors are less accurate as the response probabilities decrease.The estimatiors are more accurate at J=5 than that at J=1,which shows that the accuracy of the estimators increases as J increases based on the fractional regression imputation.