> For full details, curriculum with list of publications is available here: [.pdf]
Softwares
- pdglasso (R package): https://github.com/savranciati/pdglasso
Selection of published papers in peer-reviewed journals & volumes
- Berrettini, M., Galimberti, G., Murphy, T. B., Ranciati, S., (-), “Modelling football players field position via mixture of Gaussians with flexible weights”, Post proceedings Cladag 2023 “Studies in Classification, Data Analysis and Knowledge Organization”, accepted, to appear;
- Ranciati, S., Roverato, A., (2024), “On the application of Gaussian graphical models to paired data problems”, Statistics and Computing, 34, 209, DOI: 10.1007/s11222-024-10513-6;
- Berrettini, M., Galimberti, G., Ranciati, S., Murphy, T. B., (2024), “Identifying voting patterns on Brexit in the British House of Commons: an analysis based on Bayesian mixture models with flexible concomitant covariate effects”, Journal of the Royal Statistical Society: Series C, 73 (3), 621-638;
- Ranciati, S., Vinciotti, V., Wit, E. C., Galimberti, G., (2024), “Mixtures of multivariate generalized linear models with overlapping clusters”, Bayesian Analysis, 19 (3), 843-867;
- Ranciati, S., Roverato, A., (2023). “On the application of Gaussian graphical models to paired data problems”, arXiv pre-print, https://arxiv.org/abs/2307.14160;
- Berrettini, M., Galimberti, G., Ranciati, S., (2022), “Semiparametric finite mixture of regression models with Bayesian P-splines”, Advances in Data Analysis and Classification, 17 (3), 745-775;
- Ranciati, S., Roverato, A., Luati, A., (2021), “Fused graphical lasso for brain networks with symmetries”, Journal of Royal Statistical Society: Series C, 70 (5), 1299-1322;
- Ranciati, S., Vinciotti, V., Wit, E. C., (2020), “Identifying overlapping terrorist cells from the Noordin Top actor-event network”, Annals of Applied Statistics, 14 (3), 1516-1534;
- Ranciati, S., Wit, E. C., Viroli, C., (2020), “Bayesian Smooth-and-Match strategy for ordinary differential equations models that are linear in the parameters”, Statistica Neerlandica, 74 (2), 125-144;
- Ranciati, S., Galimberti, G., Soffritti, G., (2019), “Bayesian Variable Selection in Linear Regression Models with non-normal Errors”, Statistical Methods and Applications, 28 (2), 323-358;
- Crispino, M., D’Angelo, S., Ranciati, S., Mira, A., (2018), “Understanding dependency patterns in structural and functional brain connectivity through fMRI and DTI data”, In: Canale A., Durante D., Paci L., Scarpa B. (eds) Studies in Neural Data Science, START UP RESEARCH 2017, Springer Proceedings in Mathematics & Statistics, vol 257(1-22);
- Ranciati, S., Viroli, C., Wit, E. C., (2017), “Mixture model with multiple allocations for clustering spatially correlated observations in the analysis of ChIP-Seq data”, Biometrical Journal, 59(6), 1301-1316;
- Ranciati, S., Viroli, C., Wit, E. C., (2015), “Spatio-temporal model for multiple ChIP-Seq experiments”, Statistical Applications in Genetics and Molecular Biology, 14(2), 211-219.
