Influence of parallel computing strategies of iterative imputation of missing data: a case study on missForest
This work addresses efficiency and accuracy concerns for researchers using parallel imputation on large datasets, though it is incremental as it builds on existing methods.
The study investigated how parallel computing strategies affect the iterative imputation method missForest for missing data, finding that while both strategies had similar error rates, the variable-wise approach introduced biases in statistical estimates like means and regression coefficients.
Machine learning iterative imputation methods have been well accepted by researchers for imputing missing data, but they can be time-consuming when handling large datasets. To overcome this drawback, parallel computing strategies have been proposed but their impact on imputation results and subsequent statistical analyses are relatively unknown. This study examines the two parallel strategies (variable-wise distributed computation and model-wise distributed computation) implemented in the random-forest imputation method, missForest. Results from the simulation experiments showed that the two parallel strategies can influence both the imputation process and the final imputation results differently. Specifically, even though both strategies produced similar normalized root mean squared prediction errors, the variable-wise distributed strategy led to additional biases when estimating the mean and inter-correlation of the covariates and their regression coefficients.