Parallel Fractional Hot-Deck Imputation and Variance Estimation for Big Incomplete Data Curing

The fractional hot-deck imputation (FHDI) is a general-purpose, assumption-free imputation method for handling multivariate missing data by filling each missing item with multiple observed values without resorting to artificially created values. The corresponding R package FHDI J. Im, I. Cho, and J. K. Kim, An R package for fractional hot deck imputation, R J., vol. 10, no. 1, pp. 140-154, 2018 holds generality and efficiency, but it is not adequate for tackling big incomplete data due to the requirement of excessive memory and long running time. As a first step to tackle big incomplete data by leveraging the FHDI, we developed a new version of a parallel fractional hot-deck imputation (named as P-FHDI) program suitable for curing large incomplete datasets. Results show a favorable speedup when the P-FHDI is applied to big datasets with up to millions of instances or 10,000 of variables. This paper explains the detailed parallel algorithms of the P-FHDI for large instances (big-n) or high-dimensionality (big-p) datasets and confirms the favorable scalability. The proposed program inherits all the advantages of the serial FHDI and enables a parallel variance estimation, which will benefit a broad audience in science and engineering.

Automated summary

The fractional hot-deck imputation (FHDI) is a general imputation method for handling multivariate missing data. However, it lacks efficiency when dealing with big incomplete data. To overcome this limitation, a parallel version called P-FHDI is developed, which shows favorable speedup for large incomplete datasets.