6 papers
Summarising mortality data with a time-dependent beta latent variable model
Pedro Menezes de Araújo, Isobel Claire Gormley, Thomas Brendan Murphy
Age-specific probabilities of death provide a snapshot of population mortality at the country level at a given point in time. Due to the high dimensionality of the data, summarisin…
Clustering country-level all-cause mortality data: a review
Pedro Menezes de Araujo, Isobel Claire Gormley, Thomas Brendan Murphy
Mortality data are relevant to demography, public health, and actuarial science. Whilst clustering is increasingly used to explore patterns in such data, no study has reviewed its…
Model-based Clustering for Network Data via a Latent Shrinkage Position Cluster Model
Xian Yao Gwee, Isobel Claire Gormley, Michael Fop
Low-dimensional representation and clustering of network data are tasks of great interest across various fields. Latent position models are routinely used for this purpose by assum…
Missing data imputation using a truncated Gaussian infinite factor model with application to metabolomics data
Kate Finucane, Lorraine Brennan, Roberta De Vito +2
Metabolomics is the study of small molecules in biological samples. Metabolomics data are typically high-dimensional and contain highly correlated variables and frequent missing va…
Integrated differential analysis of multi-omics data using a joint mixture model: idiffomix
Koyel Majumdar, Florence Jaffrézic, Andrea Rau +2
Gene expression and DNA methylation are two interconnected biological processes and understanding their relationship is important in advancing understanding in diverse areas, inclu…
A consensus-constrained parsimonious Gaussian mixture model for clustering hyperspectral images
Ganesh Babu, Aoife Gowen, Michael Fop +1
The use of hyperspectral imaging to investigate food samples has grown due to the improved performance and lower cost of instrumentation. Food engineers use hyperspectral images to…