This is not the latest version of this item. The latest version can be found here.
Loading...
The wild bootstrap resampling in regression imputation algorithm with a Gaussian Mixture Model
Mat Jasin, A. Neagu, Daniel Csenki, Attila
Mat Jasin, A.
Neagu, Daniel
Csenki, Attila
Publication Date
2018-07
End of Embargo
Supervisor
Rights
© Springer International Publishing AG, part of Springer Nature 2018. Reproduced in accordance with the publisher's self-archiving policy.
Peer-Reviewed
Yes
Open Access status
openAccess
Accepted for publication
2018-07-15
Institution
Department
Awarded
Embargo end date
Additional title
Abstract
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.
Version
Accepted manuscript
Citation
Mat Jasin A, Neagu D and Csenki A (2018) The wild bootstrap resampling in regression imputation algorithm with a Gaussian Mixture Model. In: Perner P (Ed.) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science, vol 10935. Springer, Cham: 219-230.
Link to publisher’s version
Link to published version
Link to Version of Record
Type
Conference paper