Papers by Keyword: Multiple Imputation

Abstract: Hourly measured PM₁₀ concentration at eight monitoring stations within peninsular Malaysia in 2006 was used to conduct the simulated missing data. The gap lengths of the simulated missing values are limited to 12 hours since the actual trend of missingness is considered short. Two percentages of simulated missing gaps were generated that are 5 % and 15 %. A number of single imputation methods (linear interpolation (LI), nearest neighbour interpolation (NN), mean above below (MAB), daily mean (DM), mean 12-hour (12M), mean 6-hour (6M), row mean (RM) and previous year (PY)) were calculated to fill in the simulated missing data. In addition, multiple imputation (MI) was also conducted to compare between the single imputation methods. The performances were evaluated using four statistical criteria namely mean absolute error, root mean squared error, prediction accuracy and index of agreement. The results show that 6M perform comparably well to LI. Thus, this show that the effect of smaller averaging time gives better prediction. Other single imputation methods predict the missing data well except for PY. RM and MI performs moderately with the increasing performance in higher fraction of missing gaps whereas LR makes the worst methods for both simulated missing data percentages.

Abstract: The major environmental loads of mineral separation process in China iron production (with missing data) are analyzed. And the inner relationship between these loads data is qualified and the missing data are imputed using a statistic method called multiple imputation (MI), aimed to improve the quality of LCA datasets and allow industry to easily conduct a highly reliable LCA. By using computer simulation, MI replaces each missing value with a set of plausible values which represent the uncertainty of the missing data. The multiply imputed datasets are then analyzed by the standard procedures for completing data and combining the results from these analyses. The result proves that MI Method is an effective and reasonable method to solve the problem of missing data and therefore can ensure the validity and reliability of LCA.