Cappozzo, A., Casa, A. & Fop, M. (2024+)
Sparse model-based clustering of three-way data via lasso-type
penalties
Journal of Computational and Graphical Statistics
(in press) (Link)
Ferraccioli, F., Casa, A. & Stefanucci, M.
(2024)
An adaptive functional regression framework for locally
heterogeneous signals in spectroscopy
Journal of the Royal
Statistical Society: Series C, 73(5), 1370-1388 (Link)
Casa, A., Cappozzo, A. & Fop, M. (2022)
Group-wise shrinkage estimation in penalized model-based
clustering.
Journal of Classification, 39(3), 648-674 (Link)
Casa, A., O’Callaghan, T.F. & Murphy, T.B.
(2022).
Parsimonious Bayesian Factor Analysis for modelling
latent structures in spectroscopy data.
Annals of Applied
Statistics, 16(4), 2417–2436 (Link)
Casa, A. & Menardi, G. (2022).
Nonparametric semi-supervised classification with application to
signal detection in high energy physics.
Statistical Methods
& Applications, 31(3), 531–550 (Link)
Casa, A., Bouveyron, C., Erosheva, E. &
Menardi, G. (2021).
Co-clustering of time-dependent data via
the Shape Invariant Model.
Journal of Classification, 38(3),
626-649 (Link)
Casa, A., Scrucca, L. & Menardi, G. (2021).
Better than the best? Answers via model ensemble in
density-based clustering.
Advances in Data Analysis and
Classification, 15, 599-623. (Link)
Casa, A., Chacon, J.E. & Menardi, G. (2020).
Modal clustering asymptotics with applications to bandwidth
selection.
Electronic Journal of Statistics, 14(1), 835-856.
(Link)
Casa, A., Fop, M. & D’Angelo, S. (2024)
Contributed discussion on “Sparse Bayesian factor analysis when the
number of factors is unknown” by Frühwirth-Schnatter, S., Hosszejni, D.
& Freitas Lopes, H.
Bayesian Analysis (in press) (Link)
Casa, A., Fop, M. & Murphy, T.B. (2021).
Contributed Discussion on “Centered partition processes:
informative priors for clustering” by Paganin, S., Herring, A.H.,
Olshan, A.F. & Dunson, D.B.
Bayesian Analysis, 16(1),
301-370. (Link)
Frizzarin, M., Hayes, E., Casa, A. & Berry,
D.P. (2025)
Comparison of the mid-infrared spectra and
prediction equations development from morning and evening milk samples
from a twice-a-day milked dairy cows
Journal of Dairy Science,
108(2), 1573-1583
Frizzarin, M., Visentin, G., […], Zappaterra, M. &
Casa, A. (2023)
Classification of cow diet
based on milk Mid Infrared Spectra: A data analysis competition at the
“International Workshop on Spectroscopy and Chemometrics 2022”
Chemometrics and Intelligent Laboratory Systems, 104755 (Link)
Frizzarin, M., Gormley, I.C., Casa, A. &
McParland, S. (2021)
Selecting milk spectra to develop
equations to predict milk technological traits.
Foods, 10(12),
3084.
Stakia, A., Dorigo, T., Banelli, G., Bortoletto, D.,
Casa, A., […], Vischia, P. & Weiler, A. (2021)
Advanced multi-variate analysis methods for new physics searches at
the Large Hadron Collider.
Reviews in Physics, 7, 100063 (Link)
Frizzarin, M., Bevilacqua, A., […], Stefanucci, M. &
Casa, A. (2021)
Mid infrared spectroscopy and
milk quality traits: a data analysis competition at the “International
Workshop on Spectroscopy and Chemometrics 2021”
Chemometrics
and Intelligent Laboratory Systems, 219, 104442 (Link)
Frizzarin, M., O’Callaghan, T.F., Murphy, T.B., Hennessy, D.
& Casa, A. (2021).
Application of machine
learning methods to milk mid-infrared spectra for discrimination of cows
milk from pasture or total mixed ration diets.
Journal of
Dairy Science, 104(12), 12394-12402 (Link).
Frizzarin, M., Gormley, I.C., Berry, D.P., Murphy, T.B.,
Casa, A., Lynch, A. & McParland, S. (2021).
Predicting cow milk quality traits from routinely available milk
spectra using statistical machine learning methods.
Journal of
Dairy Science, 104(7), 7438–7447. (Link)
For the complete list of refereed conference proceedings, refer to the curriculum vitae