In journals
Sottosanti, A., Bovo, E., Belloni, P., and Boccuzzo, G. (2025). Bayesian mapping of mortality clusters. Biostatistics. In press. (preprint)
Sottosanti, A., Denti, F., Galimberti, S., Risso, D., and Capitoli, G. (2025). Spatially informed nonnegative matrix trifactorization for coclustering mass spectrometry data. Biometrical Journal 67(2):e70031. (link)
Montin, A., Brazzale, A. R., Menardi, G., and Sottosanti, A. (2024). Locating \(\gamma\)-ray sources on the celestial sphere via modal clustering. Statistical Methods & Applications 33:153–172. (link)
Ianniello, A., Sottosanti, A., Borriello, G., and Vincenti, M. (2023). Perception of Quality of Life and Fatigue in Multiple Sclerosis Patients Treated with High-Dose Vitamin D. Clinical and Translational Neuroscience 7(2):12-27. (link)
Righelli, D., Sottosanti, A., and Risso, D. (2023). Designing spatial transcriptomic experiments. Nature Methods 20(3):355-356. (link)
Sottosanti, A., and Risso, D. (2023). Co-clustering of Spatially Resolved Transcriptomic Data. The Annals of Applied Statistics 17(2):1444-1468. (link)
Sottosanti, A., Risso, D., and Castiglione, C. (2022). Contributed discussion: “Bayesian Nonstationary and Nonparametric Covariance Estimation for Large Spatial Data” by B. Kidd and M. Katzfuss. Bayesian Analysis 17(1):337-339. (link)
Costantin, D., Sottosanti, A., Brazzale, A. R., Bastieri, D., Fan, J. (2022). Bayesian Mixture Modeling of the High-Energy Photon Counts collected by the Fermi Large Area Telescope. Statistical Modelling 22(3):175-198. (link)
Hayden, E., Airoldi, C., Scotti, L., Bellan, M., Sottosanti, A., Bergamasco, P., Salamina, L., Capponi, A., Sainaghi, P.P., and Pirisi, M. (2021). Modelling hospital bed necessity for COVID-19 patients during the decline phase of the epidemic trajectory. Minerva Respiratory Medicine 60(2):29-35. (link)
In volumes
Sottosanti, A., Costantin, D., Bastieri, D., and Brazzale, A. R. (2019). Discovering and Locating High-Energy Extra-Galactic Sources by Bayesian Mixture Modelling. New Statistical Developments in Data Science (A. Petrucci, F. Racioppi and R. Verde Eds.), Springer Proceedings in Mathematics & Statistics, 288, 135-148. (link)
Caponera, A., Denti, F., Rigon, T., Sottosanti, A., and Gelfand, A. (2018). Hierarchical Spatio- Temporal Modeling of Resting State fMRI Data. Studies in Neural Data Science (A. Canale, D. Durante, L. Paci and B. Scarpa Eds.), Springer Proceedings in Mathematics & Statistics, 257, 111-130. (link)
Under review