Comparative Variance and Multiple Imputation Used for Missing Values in Land Price DataSet

Based on the two-dimensional relation table, this paper studies the missing values in the sample data of land price of Shunde District of Foshan City. GeoDa software was used to eliminate the insignificant factors by stepwise regression analysis; NORM software was adopted to construct the multiple imputation models; EM algorithm and the augmentation algorithm were applied to fit multiple linear regression equations to construct five different filling datasets. Statistical analysis is performed on the imputation data set in order to calculate the mean and variance of each data set, and the weight is determined according to the differences. Finally, comprehensive integration is implemented to achieve the imputation expression of missing values. The results showed that in the three missing cases where the PRICE variable was missing and the deletion rate was 5%, the PRICE variable was missing and the deletion rate was 10%, and the PRICE variable and the CBD variable were both missing. The new method compared to the traditional multiple filling methods of true value closer ratio is 75% to 25%, 62.5% to 37.5%, 100% to 0%. Therefore, the new method is obviously better than the traditional multiple imputation methods, and the missing value data estimated by the new method bears certain reference value.