4 citations · 7 across the 6 of their papers we have counts for
8 papers
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…
On Fractionally-Supervised Classification: Weight Selection and Extension to the Multivariate t-Distribution
Michael P. B. Gallaugher, Paul D. McNicholas
Recent work on fractionally-supervised classification (FSC), an approach that allows classification to be carried out with a fractional amount of weight given to the unlabelled poi…
Subspace Clustering with the Multivariate-t Distribution
Angelina Pesevski, Brian C. Franczak, Paul D. McNicholas
Clustering procedures suitable for the analysis of very high-dimensional data are needed for many modern data sets. In model-based clustering, a method called high-dimensional data…
ContaminatedMixt: An R Package for Fitting Parsimonious Mixtures of Multivariate Contaminated Normal Distributions
Antonio Punzo, Angelo Mazza, Paul D. McNicholas
We introduce the R package ContaminatedMixt, conceived to disseminate the use of mixtures of multivariate contaminated normal distributions as a tool for robust clustering and clas…
Mixtures of Multivariate Power Exponential Distributions
Utkarsh J. Dang, Ryan P. Browne, Paul D. McNicholas
An expanded family of mixtures of multivariate power exponential distributions is introduced. While fitting heavy-tails and skewness has received much attention in the model-based…
Variable Selection for Clustering and Classification
Jeffrey L. Andrews, Paul D. McNicholas
As data sets continue to grow in size and complexity, effective and efficient techniques are needed to target important features in the variable space. Many of the variable selecti…