The wild bootstrap resampling in regression imputation algorithm with a Gaussian Mixture Model

Description

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.

Links & Downloads

1. 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.
2. http://hdl.handle.net/10454/16593
3. https://doi.org/10.1007/978-3-319-96133-0_17

Tags

Missing data imputation

Gaussian Mixture Model

Bootstrap

Additional Fields

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