Rosario Barone

Assistant Professor (RTT, Tenure-Track) of Statistics
Department of Statistical Sciences
Università Cattolica del Sacro Cuore
rosario.barone@unicatt.it
Largo Fra Agostino Gemelli, 1, 20123 Milan, Italy

Short Bio

I hold an MSc in Econometrics (2016) from the University of Rome Tor Vergata and a PhD in Statistics (2020) from Sapienza University of Rome. I held postdoctoral positions in Statistics, first at Sapienza University of Rome and then at the University of Rome Tor Vergata, where I served as an Assistant Professor from March 2023 to June 2025. Since July 2025, I have been a tenure-track Assistant Professor at the Department of Statistical Sciences, Faculty of Economics, Università Cattolica del Sacro Cuore, Milan. My academic journey also includes international experience as a Visiting PhD Student at the University of Plymouth, UK (2018), and as a Visiting Assistant Professor at the University of California, Los Angeles, USA (2025).

Research

My research focuses on methodological statistics and adopts a Bayesian perspective to develop efficient methods for flexible and interpretable models suited to complex phenomena. In particular, it addresses the following topics:

Bayesian Inference for Latent and Multi-State Processes

I work on Bayesian methods for continuous-time multi-state processes, such as semi-Markov and inhomogeneous Markov models, both under partial or irregular observation and with latent state structures. My research focuses on strategies to improve inference through data augmentation and efficient simulation techniques.

Point Processes and Temporal Dependence

I am interested in modeling recurrent events and time-dependent phenomena using point processes, with a focus on latent dynamics and interactions between events over time.

Continuous time multi-state model — The continuous-time trajectory of a semi-Markov multi-state model, observed at discrete time points, simulated using the method proposed by Barone and Tancredi (2022).

Bayesian Nonparametrics

I study clustering approaches based on Bayesian nonparametric models, with a particular interest in Random Partition Models (RPMs). These models allow for flexible inference on the number and composition of clusters by placing a prior directly on the space of partitions, rather than fixing the number of clusters a priori. My research is primarily devoted to the development of novel models and implementation of efficient computational strategies for posterior inference.

Interactive 3D plot of a Gaussian copula density surface with positive dependence.

Copulas and Dependence Structures

I explore flexible models for multivariate dependence, including Bayesian formulations of copula models and vine copula constructions. My research pays particular attention to the role of covariates and non-exchangeable structures in defining joint behavior across dimensions.

High-Dimensional Bayesian Modeling and Variable Selection

I work on Bayesian models for high-dimensional data, with a focus on variable selection and regularization through structured priors. I am particularly interested in how prior assumptions affect model performance when covariates are correlated or redundant.

Publications

Please refer to my profiles for a complete list:

Latest Publications

R. Barone and A. Farcomeni (2025). Latent class multi-state quantile regression with a cure fraction: application to jail recidivism in the USA, Journal of the Royal Statistical Society (Series A), to appear.

Under Review

R. Barone and A. Tancredi. Dirichlet process multi-state mixture models.
R. Barone, F. Bartolucci, A. Farcomeni and S. Pandolfi. Multilayer dynamic stochastic block models for analyzing international arms trade patterns.

Teaching

A.Y. 2024/2025

Statistical Tools for Decision Making – Bachelor Degree in Global Governance, University of Rome Tor Vergata

A.Y. 2023/2024

Statistical Tools for Decision Making – Bachelor Degree in Global Governance, University of Rome Tor Vergata
Biostatistics – Master in Musculoskeletal and Rheumatologic Physiotherapy, University of Rome Tor Vergata
Unsupervised Learning – Master in Data Science for Public Decision Making, University of Rome Tor Vergata

A.Y. 2022/2023

Statistical Learning – Bachelor Degree in Global Governance, University of Rome Tor Vergata
Statistical Inference – PhD in Data Science, University of Rome Tor Vergata

A.Y. 2021/2022

Quantitative Methods III – Bachelor Degree in Business Administration and Economics, University of Rome Tor Vergata

About Ross

I grew up in Nocera — known as Nuvkrinum Alafaternum in Oscan and Nuceria in Latin — a historic city nestled between Naples, Salerno, and the Amalfi Coast. I owe much to Nocera, especially my lifelong friendships and my enduring passion for Nocerina, the local football team that I proudly support.

Beyond statistics, I am passionate about cooking, wine, football (more of an obsession than a passion), nature, and music. But above all, I love dogs — all of them. A special mention goes to Trilli, my little angel wagging her tail among the clouds, and Carlotta, who stole my heart in just a few seconds. I am also deeply committed to civil and social rights, which, sadly, can no longer be taken for granted.