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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About me
About me
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Posts
Future Blog Post
Published:
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Blog Post number 4
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
publications
Model-based Feature Selection and Clustering of RNA-seq Data for Unsupervised Subtype Discovery
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
Unsupervised Imputation of Non-ignorably Missing Data Using Importance-Weighted Autoencoders
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
Deeply-Learned Generalized Linear Models with Missing Data
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
software
FSCseq
Simultaneous Feature Selection and Clustering of RNA-seq
NIMIWAE
Non-Ignorably Missing Importance-Weighted Autoencoder
dlglm
Deeply-Learned Generalized Linear Models with MNAR Missingness