4 citations · 10 across the 19 of their papers we have counts for
4 papers · 1 filter
Parsimonious Mixtures of Matrix Variate Bilinear Factor Analyzers
Michael P. B. Gallaugher, Paul D. McNicholas
Over the years, data have become increasingly higher dimensional, which has prompted an increased need for dimension reduction techniques. This is perhaps especially true for clust…
Assessing and Visualizing Matrix Variate Normality
Nikola Pocuca, Michael P. B. Gallaugher, Katharine M. Clark +1
A framework for assessing the matrix variate normality of three-way data is developed. The framework comprises a visual method and a goodness of fit test based on the Mahalanobis s…
Clustering Higher Order Data: An Application to Pediatric Multi-variable Longitudinal Data
Peter A. Tait, Paul D. McNicholas, Joyce Obeid
Physical activity levels are an important predictor of cardiovascular health and increasingly being measured by sensors, like accelerometers. Accelerometers produce rich multivaria…
Clustering Discrete-Valued Time Series
Tyler Roick, Dimitris Karlis, Paul D. McNicholas
There is a need for the development of models that are able to account for discreteness in data, along with its time series properties and correlation. Our focus falls on INteger-v…