128 citations · 624 across the 30 of their papers we have counts for
33 papers
A novel family of beta mixture models for the differential analysis of DNA methylation data: an application to prostate cancer
Koyel Majumdar, Romina Silva, Antoinette Sabrina Perry +5
Identifying differentially methylated cytosine-guanine dinucleotide (CpG) sites between benign and tumour samples can assist in understanding disease. However, differential analysi…
Identifying Brexit voting patterns in the British House of Commons: an analysis based on Bayesian mixture models with flexible concomitant covariate effects
Marco Berrettini, Giuliano Galimberti, Saverio Ranciati +1
Brexit and its implications are an ongoing topic of interest since the Brexit referendum in 2016. In 2019 the House of commons held a number of "indicative" and "meaningful" votes…
Unobserved classes and extra variables in high-dimensional discriminant analysis
Michael Fop, Pierre-Alexandre Mattei, Charles Bouveyron +1
In supervised classification problems, the test set may contain data points belonging to classes not observed in the learning phase. Moreover, the same units in the test data may b…
Parsimonious Bayesian Factor Analysis for modelling latent structures in spectroscopy data
Alessandro Casa, Tom F. O'Callaghan, Thomas Brendan Murphy
In recent years animal diet has been receiving increased attention, in particular examining the impact of pasture-based feeding strategies on the quality of milk and dairy products…
Robust variable selection in the framework of classification with label noise and outliers: applications to spectroscopic data in agri-food
Andrea Cappozzo, Ludovic Duponchel, Francesca Greselin +1
Classification of high-dimensional spectroscopic data is a common task in analytical chemistry. Well-established procedures like support vector machines (SVMs) and partial least sq…
Robust variable selection for model-based learning in presence of adulteration
Andrea Cappozzo, Francesca Greselin, Thomas Brendan Murphy
The problem of identifying the most discriminating features when performing supervised learning has been extensively investigated. In particular, several methods for variable selec…