2 citations · 3 across the 5 of their papers we have counts for
6 papers
Logistic Normal Multinomial Factor Analyzers for Clustering Microbiome Data
Wangshu Tu, Sanjeena Subedi
The human microbiome plays an important role in human health and disease status. Next generating sequencing technologies allow for quantifying the composition of the human microbio…
A Family of Mixture Models for Biclustering
Wangshu Tu, Sanjeena Subedi
Biclustering is used for simultaneous clustering of the observations and variables when there is no group structure known \textit{a priori}. It is being increasingly used in bioinf…
Infinite mixtures of multivariate normal-inverse Gaussian distributions for clustering of skewed data
Yuan Fang, Dimitris Karlis, Sanjeena Subedi
Mixtures of multivariate normal inverse Gaussian (MNIG) distributions can be used to cluster data that exhibit features such as skewness and heavy tails. However, for cluster analy…
A Bayesian approach for clustering skewed data using mixtures of multivariate normal-inverse Gaussian distributions
Yuan Fang, Dimitris Karlis, Sanjeena Subedi
Non-Gaussian mixture models are gaining increasing attention for mixture model-based clustering particularly when dealing with data that exhibit features such as skewness and heavy…
A parsimonious family of multivariate Poisson-lognormal distributions for clustering multivariate count data
Sanjeena Subedi, Ryan Browne
Multivariate count data are commonly encountered through high-throughput sequencing technologies in bioinformatics, text mining, or in sports analytics. Although the Poisson distri…
A Multivariate Poisson-Log Normal Mixture Model for Clustering Transcriptome Sequencing Data
Anjali Silva, Steven J. Rothstein, Paul D. McNicholas +1
High-dimensional data of discrete and skewed nature is commonly encountered in high-throughput sequencing studies. Analyzing the network itself or the interplay between genes in th…