20 citations · 35 across the 10 of their papers we have counts for
11 papers
Model-based clustering for covariance matrices via penalized Wishart mixture models
Andrea Cappozzo, Alessandro Casa
Covariance matrices provide a valuable source of information about complex interactions and dependencies within the data. However, from a clustering perspective, this information h…
Uncover mortality patterns and hospital effects in COVID-19 heart failure patients: a novel Multilevel logistic cluster-weighted modeling approach
Luca Caldera, Chiara Masci, Andrea Cappozzo +4
Evaluating hospitals' performance and its relation to patients' characteristics is of utmost importance to ensure timely, effective, and optimal treatment. Such a matter is particu…
Sparse model-based clustering of three-way data via lasso-type penalties
Andrea Cappozzo, Alessandro Casa, Michael Fop
Mixtures of matrix Gaussian distributions provide a probabilistic framework for clustering continuous matrix-variate data, which are becoming increasingly prevalent in various fiel…
Variational Inference for Semiparametric Bayesian Novelty Detection in Large Datasets
Luca Benedetti, Eric Boniardi, Leonardo Chiani +4
After being trained on a fully-labeled training set, where the observations are grouped into a certain number of known classes, novelty detection methods aim to classify the instan…
A general framework for penalized mixed-effects multitask learning with applications on DNA methylation surrogate biomarkers creation
Andrea Cappozzo, Francesca Ieva, Giovanni Fiorito
Recent evidence highlights the usefulness of DNA methylation (DNAm) biomarkers as surrogates for exposure to risk factors for non-communicable diseases in epidemiological studies a…
Group-wise shrinkage estimation in penalized model-based clustering
Alessandro Casa, Andrea Cappozzo, Michael Fop
Finite Gaussian mixture models provide a powerful and widely employed probabilistic approach for clustering multivariate continuous data. However, the practical usefulness of these…