Yes / Unsupervised learning of finite Gaussian mixture model (FGMM) is used to learn the distribution of population data. This paper proposes the use of the wild bootstrapping to create the variability of the imputed data in single miss-ing data imputation. We compare the performance and accuracy of the proposed method in single imputation and multiple imputation from the R-package Amelia II using RMSE, R-squared, MAE and MAPE. The proposed method shows better performance when compared with the multiple imputation (MI) which is indeed known as the golden method of missing data imputation techniques.
Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/16593 |
Date | 08 July 2018 |
Creators | Mat Jasin, A., Neagu, Daniel, Csenki, Attila |
Source Sets | Bradford Scholars |
Language | English |
Detected Language | English |
Type | Conference paper, Accepted manuscript |
Rights | © Springer International Publishing AG, part of Springer Nature 2018. Reproduced in accordance with the publisher's self-archiving policy., Unspecified |
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