根據插補後數值與真實值比較，本研究最推薦EM插補法作為當資料遇到遺失資料時的插補方法，迴歸插補法則為第二推薦之插補方法、接著為MCMC插補法；另外，由於平均數插補法容易受極端值影響，因此，若資料遇到需要插補狀況時，較不推薦以平均數插補法處理該筆資料。此外，就遺失值插補方法在最適資產配置投資組合的應用上，以累績報酬率為指標所得之表現與上述插補能力一致，顯示插補方法應用在最是資產配置投資組合的重要性。 Data missing is a prevail problem for most of the data analysis. This thesis mainly focuses on how the remedy strategies, data imputation, could affect the optimal assets allocation problems. At first, 10%, 30% and 50% data are removed artificially and randomly from a complete data set. Then four different methods, mean, EM, Regression and MCMC, are employed to impute the data respectively. Then the four imputed data sets are adopted by the optimal assets allocation problems by incorporating the input from three mean estimation models and two variance estimation models.
Empirical evidence shows that EM method outperforms the rest imputation methods in terms of the accuracy. Although not as good as EM method, Regression method also performs well especially compare with MCMC method. Mean estimation is quite sensitive to the extreme data and hence is unstable and not recommended. Besides, the investment performance through the aforementioned four imputation methods in optimal assets allocation problems follow the same pattern in terms of the cumulative rate of return. It identifies the importance of the imputation methods in the optimal assets allocation problems application.