Published in Journal of Computational and Graphical Statistics, 2023
Supervised Learning in the presence of missing not at random data using a deeply-learned neural networks.
Recommended citation: D.K. Lim, N.U. Rashid, J.B. Oliva, J.G. Ibrahim (2023). "Deeply-Learned Generalized Linear Models with Missing Data" Journal of Computational and Graphical Statistics https://www.tandfonline.com/doi/full/10.1080/10618600.2023.2276122
Published in arXiv, 2021
Imputation of missing not at random data using a deeply-learned autoencoder.
Recommended citation: D.K. Lim, N.U. Rashid, J.B. Oliva, J.G. Ibrahim (2022). "Unsupervised Imputation of Non-ignorably Missing Data Using Importance-Weighted Autoencoders" arXiv https://arxiv.org/abs/2101.07357
Published in Annals of Applied Statistics, 2020
Simultaneous variable selection and clustering of samples, with adjustment for technical factors like differences in sequencing depth, and covariates like batch.
Recommended citation: D.K. Lim, N.U. Rashid, J.G. Ibrahim (2020). "Model-based Feature Selection and Clustering of RNA-seq Data for Unsupervised Subtype Discovery" Annals of Applied Statistics. http://dx.doi.org/10.1214/20-AOAS1407