Abstract:
A general-purpose, assumption-free imputation method for multivariate missing data, fractional hot-deck imputation (FHDI) fills each missing item with multiple observed values without using artificial values. 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 is general and efficient, but it requires too much memory and runs too slowly for big incomplete data.
We developed a new parallel fractional hot-deck imputation (P-FHDI) program to cure large incomplete datasets using the FHDI as a first step. P-FHDI speeds up big datasets with millions of instances or 10,000 variables.
This project describes the P-FHDI’s parallel algorithms for big-n or big-p datasets and confirms its good scalability. The proposed program inherits all the benefits of the serial FHDI and allows parallel variance estimation, benefiting a wide science and engineering audience.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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