53 citations · 120 across the 17 of their papers we have counts for
20 papers
Latent-Class Poisson-Lognormal Mixed Models for Clustering Multivariate Longitudinal Count Data
Pengqi Liu, Utkarsh J. Dang, Sanjeena Subedi
Multivariate longitudinal count data arise in biological studies, where responses may be over-dispersed, correlated within subjects and over time, measured at irregular times, and…
Mixtures of spatial factor analyzers for tensor-variate data
Hanzhang Lu, Keiran Malott, Kirsty Milligan +10
A mixture of spatial factor analyzers (MSFA) is introduced to address the challenges of clustering high-dimensional spatial data. The proposed framework utilizes the given coordina…
Spatial Covariance Constraints for Gaussian Mixture Models
Hanzhang Lu, Keiran Malott, Venkat Suprabath Bitra +11
Although extensive research exists in spatial modeling, few studies have addressed finite mixture model-based clustering methods for spatial data. Finite mixture models, especially…
Model-based bi-clustering using multivariate Poisson-lognormal with general block-diagonal covariance matrix and its applications
Caitlin Kral, Evan Chance, Ryan Browne +1
While several Gaussian mixture models-based biclustering approaches currently exist in the literature for continuous data, approaches to handle discrete data have not been well res…
Extending Cluster-Weighted Factor Analyzers for multivariate prediction and high-dimensional interpretability
Xiaoke Qin, Francesca Martella, Sanjeena Subedi
Cluster-weighted factor analyzers (CWFA) are a versatile class of mixture models designed to estimate the joint distribution of a random vector that includes a response variable al…
Finite Mixtures of Multivariate Poisson-Log Normal Factor Analyzers for Clustering Count Data
Andrea Payne, Anjali Silva, Steven J. Rothstein +2
A mixture of multivariate Poisson-log normal factor analyzers is introduced by imposing constraints on the covariance matrix, which resulted in flexible models for clustering purpo…
Tackling the infinite likelihood problem when fitting mixtures of shifted asymmetric Laplace distributions
Yuan Fang, Brian C. Franczak, Sanjeena Subedi
Mixtures of shifted asymmetric Laplace distributions were introduced as a tool for model-based clustering that allowed for the direct parameterization of skewness in addition to lo…
Estimation of Gaussian Bi-Clusters with General Block-Diagonal Covariance Matrix and Applications
Anastasiia Livochka, Ryan Browne, Sanjeena Subedi
Bi-clustering is a technique that allows for the simultaneous clustering of observations and features in a dataset. This technique is often used in bioinformatics, text mining, and…
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…
Clustering microbiome data using mixtures of logistic normal multinomial models
Yuan Fang, Sanjeena Subedi
Discrete data such as counts of microbiome taxa resulting from next-generation sequencing are routinely encountered in bioinformatics. Taxa count data in microbiome studies are typ…
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…
Finite mixtures of matrix-variate Poisson-log normal distributions for three-way count data
Anjali Silva, Steven J. Rothstein, Paul D. McNicholas +2
Three-way data structures, characterized by three entities, the units, the variables and the occasions, are frequent in biological studies. In RNA sequencing, three-way data struct…
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…
Variational Bayes Approximations for Clustering via Mixtures of Normal Inverse Gaussian Distributions
Sanjeena Subedi, Paul D. McNicholas
Parameter estimation for model-based clustering using a finite mixture of normal inverse Gaussian (NIG) distributions is achieved through variational Bayes approximations. Univaria…
Constrained Optimization for a Subset of the Gaussian Parsimonious Clustering Models
Ryan P. Browne, Sanjeena Subedi, Paul McNicholas
The expectation-maximization (EM) algorithm is an iterative method for finding maximum likelihood estimates when data are incomplete or are treated as being incomplete. The EM algo…
A Variational Approximations-DIC Rubric for Parameter Estimation and Mixture Model Selection Within a Family Setting
Sanjeena Subedi, Paul D. McNicholas
Mixture model-based clustering has become an increasingly popular data analysis technique since its introduction over fifty years ago, and is now commonly utilized within a family…
Clustering and Classification via Cluster-Weighted Factor Analyzers
Sanjeena Subedi, Antonio Punzo, Salvatore Ingrassia +1
In model-based clustering and classification, the cluster-weighted model constitutes a convenient approach when the random vector of interest constitutes a response variable Y and…