activity
20152026
most citedInteracting generalized Friedman's urn systems

20 citations · 92 across the 16 of their papers we have counts for

collaborators

17 papers

math.ST2026

A multi-factorial innovation model with mean-field interaction

Giacomo Aletti, Irene Crimaldi, Andrea Ghiglietti

We introduce an Indian-buffet-type model for multi-factorial innovation in which each arriving agent may exhibit both previously observed and new features. The number of new featur…

math.PR2026

Triggered urn models for frequently asked questions (FAQ)

Irene Crimaldi, Andrea Ghiglietti, Leen Hatem +1

We investigate a nonclassic urn model with triggers that increase the number of colors. The scheme has emerged as a model for web services that set up frequently asked questions (F…

stat.AP2025

Modeling Innovation Ecosystem Dynamics through Interacting Reinforced Bernoulli Processes

Giacomo Aletti, Irene Crimaldi, Andrea Ghiglietti +1

Innovation is cumulative and interdependent: successful inventions build on prior knowledge within technological fields and may also affect success across related ones. Yet these d…

stat.ME2025

Central limit theorems for interacting innovation processes, related statistical tools and general results

Giacomo Aletti, Irene Crimaldi, Andrea Ghiglietti

We study a networked system of innovation processes, where each process is modeled as an urn with infinitely many colors-a classical framework for capturing the emergence of novelt…

stat.ME2024

Design and inference for multi-arm clinical trials with informational borrowing: the interacting urns design

Giacomo Aletti, Alessandro Baldi Antognini, Irene Crimaldi +2

This paper deals with a new design methodology for stratified comparative experiments based on a system of interacting urns. The key idea is to model the interaction between urns f…

stat.AP2023★ 5 cited

Interacting Innovation processes: case studies from Reddit and Gutenberg

Giacomo Aletti, Irene Crimaldi, Andrea Ghiglietti

In this work, we introduce an extremely general model for a collection of innovation processes in order to model and analyze the interaction among them. We provide theoretical resu…

math.PR2022★ 1 cited

Networks of reinforced stochastic processes: probability of asymptotic polarization and related general results

Giacomo Aletti, Irene Crimaldi, Andrea Ghiglietti

In a network of reinforced stochastic processes, for certain values of the parameters, all the agents' inclinations synchronize and converge almost surely toward a certain random v…

math.PR2022★ 2 cited

Networks of reinforced stochastic processes: a complete description of the first-order asymptotics

Giacomo Aletti, Irene Crimaldi, Andrea Ghiglietti

We consider a finite collection of reinforced stochastic processes with a general network-based interaction among them. We provide sufficient and necessary conditions in order to h…

math.PR2018★ 13 cited

Interacting reinforced stochastic processes: statistical inference based on the weighted empirical means

Giacomo Aletti, Irene Crimaldi, Andrea Ghiglietti

This work deals with a system of interacting reinforced stochastic processes, where each process is located at a vertex of a finite weighted direct graph, and…

stat.ME2017

A k-means procedure based on a Mahalanobis type distance for clustering multivariate functional data

Andrea Martino, Andrea Ghiglietti, Francesca Ieva +1

This paper proposes a clustering procedure for samples of multivariate functions in , with . This method is based on a k-means algorithm in which the distance…

math.PR2017★ 7 cited

Networks of reinforced stochastic processes: asymptotics for the empirical means

Giacomo Aletti, Irene Crimaldi, Andrea Ghiglietti

This work deals with systems of interacting reinforced stochastic processes, where each process is located at a vertex of a finite weighted direct graph, and…

stat.ME2016★ 13 cited

Nonparametric covariate-adjusted response-adaptive design based on a functional urn model

Giacomo Aletti, Andrea Ghiglietti, William F. Rosenberger

In this paper we propose a general class of covariate-adjusted response-adaptive (CARA) designs based on a new functional urn model. We prove strong consistency concerning the func…

math.PR2016★ 19 cited

Synchronization of Reinforced Stochastic Processes with a Network-based Interaction

Giacomo Aletti, Irene Crimaldi, Andrea Ghiglietti

Randomly evolving systems composed by elements which interact among each other have always been of great interest in several scientific fields. This work deals with the synchroniza…

math.PR2016★ 20 cited

Interacting generalized Friedman's urn systems

Giacomo Aletti, Andrea Ghiglietti

We consider systems of interacting Generalized Friedman's Urns (GFUs) having irreducible mean replacement matrices. The interaction is modeled through the probability to sample the…

math.PR2015★ 10 cited

Dynamics of an Adaptive Randomly Reinforced Urn

Giacomo Aletti, Andrea Ghiglietti, Anand Vidyashankar

Adaptive randomly reinforced urn (ARRU) is a two-color urn model where the updating process is defined by a sequence of non-negative random vectors …

math.PR2015

Central limit Theorem for an Adaptive Randomly Reinforced Urn Model

Andrea Ghiglietti, Anand N. Vidyashankar, William F. Rosenberger

The generalized Pòlya urn (GPU) models and their variants have been investigated in several disciplines. However, typical assumptions made with respect to the GPU do not include ur…

math.ST2015★ 2 cited

On linear regression models in infinite dimensional spaces with scalar response

Andrea Ghiglietti, Francesca Ieva, Anna Maria Paganoni +1

In functional linear regression, the parameters estimation involves solving a non necessarily well-posed problem and it has points of contact with a range of methodologies, includi…