Imputation of data Missing Not at Random: Artificial generation and benchmark analysis

Author:

Pereira Ricardo CardosoORCID,Abreu Pedro HenriquesORCID,Rodrigues Pedro PereiraORCID,Figueiredo Mário A.T.ORCID

Funder

Ministério da Ciência, Tecnologia e Ensino Superior

Fundação para a Ciência e a Tecnologia

Publisher

Elsevier BV

Reference35 articles.

1. Improving accuracy of missing data imputation in data mining;Ali;Kurdistan Journal of Applied Research,2017

2. Missing data in clinical research: A tutorial on multiple imputation;Austin;Canadian Journal of Cardiology,2020

3. Characterizing and managing missing structured data in electronic health records: data analysis;Beaulieu-Jones;JMIR Medical Informatics,2018

4. Beaulieu-Jones, B. K., & Moore, J. H. (2017). Missing data imputation in the electronic health record using deeply learned autoencoders. In Pacific symposium on biocomputing 2017 (pp. 207–218).

5. A variational autoencoder solution for road traffic forecasting systems: Missing data imputation, dimension reduction, model selection and anomaly detection;Boquet;Transportation Research Part C (Emerging Technologies),2020

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