ANTONIO LIJOI

ANTONIO LIJOI

Courses a.y. 2024/2025

Biographical note

I am a Professor of Statistics and Director of the PhD program in Statistics and Computer Science.

I have previously been at the University of Pavia as Assistant (1999-2005), Associate Professor (2006-2011) of Statistics and Professor of Probability and Mathematical Statistics (2011-2016).

I am Fellow of the Institute of Mathematics Statistics and of the International Society of Bayesian Analysis. I am also Fellow of the Bocconi Institute for Data Science and Analytics.

I have gained the PhD in Statistics at the University of Trento and the University Degree in Economic and Social Disciplines (DES) at the Bocconi University.


Selected Publications

Franzolini, Beatrice; Lijoi, Antonio; Pruenster, Igor
Model selection for maternal hypertensive disorders with symmetric hierarchical Dirichlet processes
THE ANNALS OF APPLIED STATISTICS, 2023

Ascolani, Filippo; Lijoi, Antonio; Rebaudo, Giovanni; Zanella, Giacomo
Clustering consistency with Dirichlet process mixtures
BIOMETRIKA, 2023

Catalano, Marta; Lijoi, Antonio; Pruenster, Igor
Measuring dependence in the Wasserstein distance for Bayesian nonparametric models
ANNALS OF STATISTICS, 2021

Federico Camerlenghi; Antonio Lijoi; Igor Pruenster
Survival analysis via hierarchically dependent mixture hazards
ANNALS OF STATISTICS, 2021

Antonio Lijoi; Igor Pruenster; Tommaso Rigon
The Pitman-Yor multinomial process for mixture modelling
BIOMETRIKA, 2020

Camerlenghi, Federico; Lijoi, Antonio; Orbanz, Peter; Pruenster, Igor
Distribution theory for hierarchical processes
ANNALS OF STATISTICS, 2019

Federico Camerlenghi; David B. Dunson; Antonio Lijoi; Abel Rodriguez; Igor Pruenster
Latent nested nonparametric priors (with discussion)
BAYESIAN ANALYSIS, 2019

Canale Antonio; Lijoi, Antonio; Nipoti, Bernardo; Pruenster, Igor
On the Pitman-Yor process with spike and slab base measure
BIOMETRIKA, 2017

De Blasi, Pierpaolo; Favaro, Stefano; Lijoi, Antonio; Mena, Rams├ęs H.; Pruenster, Igor; Ruggiero, Matteo
Are Gibbs-type priors the most natural generalization of the Dirichlet process?
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2015