Papers

Preprints

  1. Anceschi, N., Rigon, T., Zanella, G., and Durante D. (2024+). Optimal lower bounds for logistic log-likelihoods. Submitted. [ArXiv].

  2. Ghilotti, L., Camerlenghi, F., and Rigon, T. (2024+). Bayesian analysis of product feature allocation models. Submitted. [ArXiv] [Github repository].

  3. Agnoletto, D., Rigon, T., and Dunson D.B. (2024+). Bayesian inference for generalized linear models via quasi-posteriors. Submitted. [ArXiv].

  4. Rigon, T., Scarpa, B. and Petrone, S. (2024+). Enriched Pitman-Yor processes. Submitted. [ArXiv].

Articles in refereed journals

  1. Zito, A., Rigon, T., and Dunson, D. B. (2024+). Bayesian nonparametric modeling of latent partitions via Stirling-gamma priors. Bayesian Analysis, to appear. [Link].

  2. Lijoi, A., Prünster, I. and Rigon, T. (2024). Finite-dimensional discrete random structures and Bayesian clustering. Journal of the American Statistical Association (T&M), 119(546), 929–941. [Link].

  3. Catalano, M., Lijoi, A., Prünster, I. and Rigon, T. (2023). Bayesian modeling via discrete nonparametric priors. Japanese Journal of Statistics and Data Science, 6, 607–624 [Link].

  4. Rigon, T. (2023). An enriched mixture model for functional clustering. Applied Stochastic Models in Business and Industry, 39, 232–250 [Link].

  5. Rigon, T. and Aliverti E. (2023) Conjugate priors and bias reduction for logistic regression models. Statistics and Probability Letters, 202, 109901. [Link] [Github repository].

  6. Rigon, T., Herring, A. H. and Dunson, D. B. (2023). A generalized Bayes framework for probabilistic clustering. Biometrika, 110(3), 559–578. [Link] [Github repository].

  7. Zito, A., Rigon, T. and Dunson, D. B. (2023). Inferring taxonomic placement from DNA barcoding aiding in discovery of new taxa. Methods in Ecology and Evolution, 14, 529–542 [Link] [GitHub Repository].

  8. Zito, A., Rigon, T., Ovaskainen, O. and Dunson, D. B. (2023). Bayesian modelling of sequential discoveries. Journal of the American Statistical Association (T&M), 118(544), 2521–2532. [Link] [GitHub Repository].

  9. Legramanti, S., Rigon, T., Durante, D. and Dunson D. B. (2022). Extended stochastic block models with application to criminal networks. Annals of Applied Statistics 16(4), 2369–2395. [Link] [GitHub repository].

  10. Reverberi, C., Rigon, T., Solari, A., Hassan, C., Cherubini, P., GI Genius CADx Study Group and A. Cherubini (2022). Experimental evidence of effective human-AI collaboration in medical decision‐making. Scientific Reports, 12(14952) [Link].

  11. Favaro, S., Panero, F. and Rigon, T. (2021). Bayesian nonparametric disclosure risk assessment. Electronic Journal of Statistics 15(2), 5626–5651. [Link].

  12. Rigon, T. and Durante, D., (2021), Tractable Bayesian density regression via logit stick-breaking priors. Journal of Statistical Planning and inference 211, 131–142. [Link] [GitHub Repository].

  13. Legramanti, S., Rigon, T. and Durante, D. (2020). Bayesian testing for exogenous partition structures in stochastic block models. Sankhya A: The Indian Journal of Statistics, 84, 108–126. [Link] [GitHub repository].

  14. Lijoi, A., Prünster, I. and Rigon, T. (2020). Sampling hierarchies of discrete random structures. Statistics and Computing 30, 1591–1607. [Link].

  15. Lijoi, A., Prünster, I. and Rigon, T. (2020). The Pitman–Yor multinomial process for mixture modeling. Biometrika 107(4), 891–906. [Link].

  16. Durante, D. and Rigon, T. (2019). Conditionally conjugate mean-field variational Bayes for logistic models. Statistical Science 34(3), 472–485. [Link] [GitHub Repository].

  17. Durante, D., Canale, A. and Rigon, T. (2019). A nested expectation-maximization algorithm for latent class models with covariates. Statistics and Probability Letters 146, 97–103. [Link] [GitHub Repository].

  18. Rigon, T., Durante, D. and Torelli, N. (2019). Bayesian semiparametric modelling of contraceptive behavior in India via sequential logistic regressions. Journal of the Royal Statistical Society, Series A 182(1), 225–247. [Link] [GitHub Repository].

Publications in monographs, volumes, and discussions

  1. Rigon, T., Aliverti, E., Russo, M., and Scarpa, B. (2021). A discussion on: “Centered partition processes: Informative priors for clustering” Paganin, S., Herring, A. H., Olshan, A. F., Dunson, D. B., et al. (2021) in Bayesian Analysis 16(1) 301–370. [Link].

  2. Aliverti, E., Paganin, S., Rigon, T. and Russo, M. (2019). A discussion on: “Latent nested nonparametric priors” by Camerlenghi, F., Dunson, D.B., Lijoi, A., Prünster, I. and Rodriguez, A. in Bayesian Analysis 14(4), 1303–1356. [Link].

  3. Caponera, A., Denti, F., Rigon, T., Sottosanti, A. and Gelfand, A. (2018). Hierarchical Spatio-Temporal Modeling of Resting State fMRI Data. In Studies in Neural Data Science (Canale, A., Durante, D., Paci, L. And Scarpa, B., editors). [Link] [GitHub Repository].

National conference proceedings

  1. Agnoletto, D., Rigon, T. and Scarpa, B. (2023). Bayesian density estimation for modeling age-at-death distribution. In Book of Short Papers of the Italian Statistical Society (Chelli, F. M., Ciommi, M., Ingrassia, S., Mariani, F., Recchioni, M.C.) 2023. ISBN: 9788891935618. [Link].

  2. Cogo, R., Camerlenghi, F. and Rigon, T. (2023). Hierarchical processes in survival analysis. In Book of Short Papers of the Italian Statistical Society (Chelli, F. M., Ciommi, M., Ingrassia, S., Mariani, F., Recchioni, M.C.) 2023. ISBN: 9788891935618. [Link].

  3. Presicce, L., Rigon, T. and Aliverti, E. (2023). Bias-reduction methods for Poisson regression models. In Book of Short Papers of the Italian Statistical Society (Chelli, F. M., Ciommi, M., Ingrassia, S., Mariani, F., Recchioni, M.C.) 2023. ISBN: 9788891935618. [Link].

  4. Legramanti S., Rigon, T. and Durante, D. (2022). Bayesian clustering of brain regions via extended stochastic block models. In Book of Short Papers of the Italian Statistical Society (Balzanella, A., Bini, M., Cavicchia, C., Verde, R.) 2022. ISBN: 9788891932310. [Link].

  5. Zito, A., Rigon, T. and Dunson, D. B. (2021). Modelling of accumulation curves through Weibull survival functions. In Book of Short Papers of the Italian Statistical Society 2021 (Perna, C., Salvati, N. and Schirripa Spagnolo, F., editors). ISBN: 9788891927361. [Link - Part 1] [Link - Part 2].

  6. Rigon, T. (2018). Logit stick-breaking priors for partially exchangeable count data. In Book of Short Papers SIS 2018 (Abbruzzo, A., Piacentino, D., Chiodi, M. and Brentari, E., editors). ISBN: 9788891910233. [Link].