FragmGAN: generative adversarial nets for fragmentary data imputation and prediction

References

• Awan, S. E., Bennamoun, M., Sohel, F., Sanfilippo, F., & Dwivedi, G. (2021). Imputation of missing data with class imbalance using conditional generative adversarial networks. Neurocomputing, 453(17), 164–171. https://doi.org/10.1016/j.neucom.2021.04.010
   Google Scholar
• Camino, R. D., Hammerschmidt, C. A., & State, R. (2019). Improving missing data imputation with deep generative models. arXiv:1902.10666v1.
   Google Scholar
• Dalca, A. V., Guttag, J., & Sabuncu, M. R. (2019). Unsupervised data imputation via variational inference of deep subspaces. arXiv:1903.03503v1.
   Google Scholar
• Deng, G., Han, C., & Matteson, D. S. (2020). Learning to rank with missing data via generative adversarial networks. arXiv:2011.02089v2.
   Google Scholar
• Fan, J., & Lv, J. (2008). Sure independence screening for ultrahigh dimensional feature space (with discussions). Journal of Royal Statistical Society Series B, 70(5), 849–911. https://doi.org/10.1111/j.1467-9868.2008.00674.x
   Google Scholar
• Fang, F., Lan, W., Tong, J., & Shao, J. (2019). Model averaging for prediction with fragmentary data. Journal of Business & Economic Statistics, 37(3), 517–527. https://doi.org/10.1080/07350015.2017.1383263
   Web of Science ®Google Scholar
• Friedjungová, M., Vasata, D., Balatsko, M., & Jirina, M. (2020). Missing features reconstruction using a Wasserstein generative adversarial imputation network. In International Conference on Computational Science (ICCS 2020). pp. 225–239.
   Google Scholar
• García-Laencina, P. J., Sancho-Gómez, J. -L., & Figueiras-Vidal, A. R. (2010). Pattern classification with missing data: a review. Neural Computing and Applications, 19(2), 263–282. https://doi.org/10.1007/s00521-009-0295-6
   Web of Science ®Google Scholar
• Ghalebikesabi, S., Cornish, R., Holmes, C., & Kelly, L. (2021). Deep generative missingness pattern-set mixture models. In Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021). pp. 3727–3735.
   Google Scholar
• Gondara, L., & Wang, K. (2017). Multiple imputation using deep denoising autoencoders. arXiv:1705.02737.
   Google Scholar
• Gong, Y., Hajimirsadeghi, H., He, J., Durand, T., & Mori, G. (2021). Variational selective autoencoder: Learning from partially-observed heterogeneous data. In Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021). pp. 2377–2385.
   Google Scholar
• Hwang, U., Jung, D., & Yoon, S. (2019). HexaGAN: Generative adversarial nets for real world classification. In Proceedings of the 36th International Conference on Machine Learning (ICML 2019). pp. 2921–2930.
   Google Scholar
• Ipsen, N. B., Mattei, P. -A., & Frellsen, J. (2021). NOT-MIWAE: Deep generative modelling with missing not at random data. In International Conference on Learning Representations (ICLR 2021).
   Google Scholar
• Ivanov, O., Figurnov, M., & Vetrov, D. (2019). Variational autoencoder with arbitrary conditioning. In International Conference on Learning Representations (ICLR 2019).
   Google Scholar
• Lee, D., Kim, J., Moon, W.-J., & Ye, J. C. (2019). CollaGAN: Collaborative gan for missing image data imputation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019). pp. 2487–2496.
   Google Scholar
• Li, Q., & Li, L. (2021). Integrative factor regression and its inference for multimodal data analysis. Journal of the American Statistical Association, https://doi.org/10.1080/01621459.2021.1914635.
   Web of Science ®Google Scholar
• Li, S. C. -X., Jiang, B., & Marlin, B. (2019). MisGAN: Learning from incomplete data with generative adversarial networks. In International Conference on Learning Representations (ICLR 2019).
   Google Scholar
• Lichman, M. (2013). UCI machine learning repository. http://archive.ics.uci.edu/ml.
   Google Scholar
• Lin, H., Liu, W., & Lan, W. (2021). Regression analysis with individual-specific patterns of missing covariates. Journal of Business & Economic Statistics, 39(1), 179–188. https://doi.org/10.1080/07350015.2019.1635486
   Web of Science ®Google Scholar
• Little, R. J., & Rubin, D. B. (2014). Statistical analysis with missing data. (2nd ed.).John Wiley & Sons.
   Google Scholar
• Ma, W., & Chen, H. G. (2019). Missing not at random in matrix completion: The effectiveness of estimating missingness probabilities under a low nuclear norm assumption. In 33rd Conference on Neural Information Processing Systems (NeurIPS 2019).
   Google Scholar
• Mattei, P.-A., & Frellsen, J. (2019). MIWAE: Deep generative modelling and imputation of incomplete data sets. In Proceedings of the 36th International Conference on Machine Learning (ICML 2019). pp. 4413–4423.
   Google Scholar
• Mazumder, R., Hastie, T., & Tibshirani, R. (2010). Spectral regularization algorithms for learning large incomplete matrices. Journal of Machine Learning Research, 11(Aug), 2287–2322.
   PubMedGoogle Scholar
• Neves, D. T., Naik, M. G., & Proenca, A. (2021). SGAIN, WSGAIN-CP and WSGAIN-GP: Novel GAN methods for missing data imputation. In International Conference on Computational Science (ICCS 2021). pp. 98–113.
   Google Scholar
• Qiu, W., Huang, Y., & Li, Q. (2020). IFGAN: Missing value imputation using feature-specific generative adversarial networks. In IEEE International Conference on Big Data (Big Data2020). pp. 4715–4723.
   Google Scholar
• Richardson, T. W., Wu, W., Lin, L., Xu, B., & Bernal, E. A. (2020). MCFlow: Monte carlo flow models for data imputation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020). pp. 14205–14214.
   Google Scholar
• Rubin, D. B. (2004). Multiple imputation for nonresponse in surveys. John Wiley & Sons.
   Google Scholar
• Smieja, M., Kolomycki, M., Struski, L., Juda, M., & Figueiredo, M. A. T. (2020). Iterative imputation of missing data using auto-encoder dynamics. In International Conference on Neural Information Processing (ICONIP 2020).
   Google Scholar
• Stekhoven, D. J., & Buhlmann, P. (2011). MissForest — non-parametric missing value imputation for mixed-type data. Bioinformatics, 28(1), 112–118. https://doi.org/10.1093/bioinformatics/btr597
   PubMed Web of Science ®Google Scholar
• van Buuren, S., & Groothuis-Oudshoorn, K. (2011). MICE: multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1–67.
   Web of Science ®Google Scholar
• Wang, Y., Li, D., Li, X., & Yang, M. (2021). PC-GAIN: pseudo-label conditional generative adversarial imputation networks for incomplete data. Neural Networks, 141(Sep), 395–403. https://doi.org/10.1016/j.neunet.2021.05.033
   PubMedGoogle Scholar
• Xue, F., & Qu, A. (2021). Integrating multi-source block-wise missing data in model selection. Journal of the American Statistical Association, 116(536), 1914–1927. https://doi.org/10.1080/01621459.2020.1751176
   Web of Science ®Google Scholar
• Yoon, J., Jordon, J., & van der Schaar, M. (2018). GAIN: Missing data imputation using generative adversarial nets. In Proceedings of the 35th International Conference on Machine Learning (ICML 2018). pp. 5689–5698.
   Google Scholar
• Yoon, S., & Sull, S. (2020). GAMIN: Generative adversarial multiple imputation network for highly missing data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020). pp. 8456–8464.
   Google Scholar
• You, J., Ma, X., Ding, D., Kochenderfer, M., & Leskovec, J. (2020). Handling missing data with graph representation learning. In Proceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS 2020).
   Google Scholar
• Zhang, Y., Tang, N., & Annie, Q. (2020). Imputed factor regression for high-dimensional blockwise missing data. Statistica Sinica, 30(2), 631–651.
   Web of Science ®Google Scholar