A comparison of multiple imputation with EM algorithm and MCMC method for quality of life missing data |
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Authors: | Ting Hsiang Lin |
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Institution: | 1.Department of Statistics,National Taipei University,Taipei,Taiwan, ROC |
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Abstract: | This study investigated the performance of multiple imputations with Expectation-Maximization (EM) algorithm and Monte Carlo
Markov chain (MCMC) method in missing data imputation. We compared the accuracy of imputation based on some real data and
set up two extreme scenarios and conducted both empirical and simulation studies to examine the effects of missing data rates
and number of items used for imputation. In the empirical study, the scenario represented item of highest missing rate from
a domain with fewest items. In the simulation study, we selected a domain with most items and the item imputed has lowest
missing rate. In the empirical study, the results showed there was no significant difference between EM algorithm and MCMC
method for item imputation, and number of items used for imputation has little impact, either. Compared with the actual observed
values, the middle responses of 3 and 4 were over-imputed, and the extreme responses of 1, 2 and 5 were under-represented.
The similar patterns occurred for domain imputation, and no significant difference between EM algorithm and MCMC method and
number of items used for imputation has little impact. In the simulation study, we chose environmental domain to examine the
effect of the following variables: EM algorithm and MCMC method, missing data rates, and number of items used for imputation.
Again, there was no significant difference between EM algorithm and MCMC method. The accuracy rates did not significantly
reduce with increase in the proportions of missing data. Number of items used for imputation has some contribution to accuracy
of imputation, but not as much as expected. |
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