RESEARCH

PUBLICATIONS IN REFEREED JOURNALS:

• C. Balocchi, F. Camerlenghi, S. Favaro (2024). A Bayesian nonparametric approach to species sampling problems with ordering. Bayesian Analysis, to appear. Available at:  https://arxiv.org/abs/2203.07342

• F. Camerlenghi, R. Corradin, A. Ongaro (2023). Contaminated Gibbs-type priors. Bayesian Analysis, to appear.  Available at: https://arxiv.org/abs/2108.11997

• F. Camerlenghi, S. Favaro, L. Masoero, T. Broderick (2022). Scaled process priors for Bayesian nonparametric estimation of the unseen genetic variation. Journal of the American Statistical Association, to appear. Available at:  https://arxiv.org/abs/2106.15480

• F. Denti, F. Camerlenghi, M. Guindani, A. Mira (2023).  A Common Atom Model for the Bayesian Nonparametric Analysis of Nested Data. Journal of the American Statistical Association, 118, 405-416. 

• L. Masoero, F. Camerlenghi, S. Favaro, T. Broderick (2022). More for less: predicting and maximizing genetic variant discovery via Bayesian nonparamterics.  Biometrika, 109, 17-32.

• F. Camerlenghi, S. Favaro (2021). On Johnson’s “Sufficientness” Postulates for Feature-Sampling Models. Mathematics, special issue “Bayesian Predictive Inference and Related Asymptotics—Festschrift for Eugenio Regazzini's 75th Birthday”, 9, 2891. 

• F. Ayed, M. Battiston, F. Camerlenghi, S. Favaro (2021). Consistent estimation of small masses in feature sampling. Journal of Machine Learning Research, 22, 1-28.

• F. Ayed, M. Battiston, F. Camerlenghi, S. Favaro (2021). On consistent and rate optiaml estimation of the missing mass. Annales de l'Institut de Henri Poincaré - Probabilités et Statistiques, 57, 1476-1494. 

• F. Camerlenghi, C. Macci, E. Villa (2021). Asymptotic behavior of mean density estimators based on a single observation: the Boolean model case. Annals of the Institute of  Statistical Mathematics, 73, 1011-1035. 

• F. Camerlenghi, A. Lijoi, I. Prünster (2021). Survival analysis via hierarchically dependent mixture hazards. The Annals of Statistics, 49, 863-884. 

• F. Camerlenghi, S. Favaro, Z. Naulet, F. Panero (2021). Optimal disclosure risk assessment. The Annals of Statistics, 49, 723-744. (arxiv link)

• F. Camerlenghi, B. Dumitrascu, F. Ferrari, B. E. Engelhardt, S. Favaro (2020). Bayesian nonparametric multi-armed bandits for single cell experimental design. The Annals of Applied Statistics, 14, 2003-2019.

• F. Ayed, M. Battiston, F. Camerlenghi (2020).  An information theoretic approach to post randomization methods under differential privacy.  Statistics & Computing, 30, 1347-1361.

• F. Ayed, M. Battiston, F. Camerlenghi, S. Favaro (2019). A Good-Turing estimator for feature allocation models. Electronic Journal of Statistics, 13, 3775-3804.

• F. Camerlenghi, D.B. Dunson, A. Lijoi, I. Prünster, A. Rodriguez (2019). Latent nested nonparametric priors. Bayesian Analysis, 14, 1303-1356.  (with discussion).

• F. Camerlenghi, A. Lijoi, P. Orbanz, I. Prünster (2019). Distribution theory for hierarchical processes. The Annals of Statistics, 47, 67-92.

• F. Camerlenghi, A. Lijoi, I. Prünster (2018). Bayesian nonparametric inference beyond the Gibbs-type framework. Scandinavian Journal of Statistics, 45, 1062-1091.

• F. Camerlenghi, E. Villa (2018). Large and moderate deviations for kernel-type estimators of the mean density of Boolean models. Electronic Journal of Statistics, 12, 427-460.

• F. Camerlenghi, A. Lijoi, I. Prünster (2017). Bayesian prediction with multiple-sample information. Journal of Multivariate Analysis, 156, 18-28.

• F. Camerlenghi, C. Macci, E. Villa (2016). Asymptotic results for multivariate estimators of the mean density of random closed sets. Electronic Journal of Statistics 10, 2066-2096.

• F. Camerlenghi, E. Villa (2015). Optimal bandwidth of the “Minkowski content”-based estimator of the mean density of random closed sets: theoretical results and numerical experiments. Journal of Mathematical Imaging and Vision , 53, 264-287; 

• F. Camerlenghi, V. Capasso, E. Villa (2014). Numerical experiments for the estimation of mean densities of random closed sets. Image Analysis and Stereology, 33, 83-94;

• F. Camerlenghi, V. Capasso, E. Villa (2014). On the estimation of the mean density of random closed sets. Journal of Multivariate Analysis, 125, 65-88.

CONFERENCE PROCEEDINGS:

• R. Cogo, F. Camerlenghi, T. Rigon (2023). Hierarchical processes in survival analysis. In Book of the Short Papers SIS 2023 (pp. 304-309). Pearson.

• F. Denti, F. Camerlenghi, M. Guindani, A. Mira (2022). Clustering artists based on the energy distributions of their songs on Spotify via the Common Atoms Model.  In Book of the short papers- SIS 2022 (pp. 121-126). Pearson.

• F. Camerlenghi, R. Corradin, A. Ongaro (2021). On the convex combination of a Dirichlet process with a diffuse probability measure. In Book of short papers - SIS 2021 (pp.739-744). Pearson.

• L. Masoero, F. Camerlenghi, S. Favaro, T. Broderick (2021). Bayesian nonparametric prediction: from species to features. In Book of short papers - SIS 2021 (pp.727-732). Pearson.

• F. Camerlenghi, A. Lijoi, I. Prünster (2020). Bayesian nonparametric prediction with multi-sample data. In: La Rocca M., Liseo B., Salmaso L. (eds) Nonparametric Statistics. ISNPS 2018. Springer Proceedings in Mathematics & Statistics, vol 339. Springer, Cham. 

• F. Camerlenghi, C. Carota, S. Favaro (2019). Bayesian nonparametric measures of disclosure risk. In  Book of Short Papers SIS2019 Smart Statistics for Smart Applications, 119-124.

• F. Ayed, M. Battiston, F. Camerlenghi (2019). A formal approach to data swapping and disclosure limitation techniques. In Book of Short Papers SIS2019 Smart Statistics for Smart Applications, 47-54.

• F. Camerlenghi, S. Favaro, L. Masoero (2019). Hierarchies of nonparametric priors. In Book of Short Papers SIS2019 Smart Statistics for Smart Applications, 125-132.

• F. Camerlenghi, S. Favaro, Z. Naulet, F. Panero (2019). Issues with nonparametric disclosure risk assessment. In Book of Short Papers SIS2019 Smart Statistics for Smart Applications, 133-139.

• F. Camerlenghi, A. Lijoi, I. Prünster (2018). Density estimation via hierarchies of nonparametric priors. In JSM Proceedings, Section on Bayesian Statistical Science. Alexandria, VA: American Statistical Association, 2596-2605.

• M. Battiston, F. Camerlenghi, E. Dolera, S. Favaro (2018). Estimating the number of unseen species under heavy tails. Poceedings of the 49th Scientific meeting of the Italian Statistical Society.

• F. Camerlenghi, A. Lijoi, I. Prünster (2017). On some distributional properties of hierarchical processes. In JSM Proceedings, Section on Bayesian Statistical Science. Alexandria, VA: American Statistical Association. 

• F. Camerlenghi, I. Pruenster, M. Ruggiero (2016). On time-dependent Gibbs-type random probability measures. In JSM Proceedings, Section on Nonparametric Statistics. Alexandria, VA: American Statistical Association.

OTHER PUBLICATIONS:

• F. Camerlenghi (2019). Hierarchical and nested random probability measures with statistical applications. Best Phd Thesis In Statistics And Applications. Demography. Statistics. Applied Statistics. Cleup.