Publications

Articles under review

• Casa, A., Murphy, T.B. & Fop, M. (2025+)
  A latent variable model for identifying and characterizing food adulteration
  arXiv:2512.13939 (Link)

Articles in refereed journals (Statistics)

• Gardella, J., Argiento, A., Casa, A. & Pini, A. (2026)
  Addressing phase discrepancies in functional data: a Bayesian approach for accurate alignment and smoothing
  Annals of Applied Statistics (just accepted) (Link)

• Casa, A., Ferrari, D. & Huang, Z. (2026)
  High-dimensional covariance estimation by pairwise likelihood truncation
  Biometrika, 113(1), (Link)

• Casa, A. & Ferrari, D. (2025)
  Confidence set for mixture order selection
  Statistics and Probability Letters, 226, 110509 (Link)

• Cappozzo, A. & Casa, A. (2025)
  Model-based clustering for covariance matrices via penalized Wishart mixture models
  Computational Statistics and Data Analysis, 212, 108232 (Link)

• Cappozzo, A., Casa, A. & Fop, M. (2025)
  Sparse model-based clustering of three-way data via lasso-type penalties
  Journal of Computational and Graphical Statistics, 34 (3), 1030-1050 (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)

Refereed discussions

• 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, 20(1), 213-344. (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)

Articles in refereed journals (Cross-disciplinary)

• 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)

Book Chapters

• Casa, A., Cappozzo, A. & Fop, M. (2023).
  Penalized Model-Based Clustering with Group-Dependent Shrinkage Estimation.
  Building Bridges between Soft and Statistical Methodologies for Data Science. SMPS 2022. Advances in Intelligent Systems and Computing, vol 1433. Springer. https://doi.org/10.1007/978-3-031-15509-3_10
• Cabassi A., Casa, A., Farcomeni, A., Fontana, M. & Russo, M. (2018).
  Three testing perspectives on connectome data.
  Studies in Neural Data Science, Springer Volume ‘Proceedings in Mathematics & Statistics’, ISBN-9783030000394.

Refereed Conference Proceedings

For the complete list of refereed conference proceedings, refer to the curriculum vitae