activity
20152021
most citedA priori truncation method for posterior sampling from homogeneous normalized completely random measure mixture models

2 citations · 4 across the 4 of their papers we have counts for

collaborators

5 papers

stat.ME2021

Bayesian GARCH Modeling of Functional Sports Data

Patric Dolmeta, Raffaele Argiento, Silvia Montagna

The use of statistical methods in sport analytics has gained a rapidly growing interest over the last decade, and nowadays is common practice. In particular, the interest in unders…

stat.ME2020

MCMC computations for Bayesian mixture models using repulsive point processes

Mario Beraha, Raffaele Argiento, Jesper Møller +1

Repulsive mixture models have recently gained popularity for Bayesian cluster detection. Compared to more traditional mixture models, repulsive mixture models produce a smaller num…

stat.AP2019

Bayesian isotonic logistic regression via constrained splines: an application to estimating the serve advantage in professional tennis

Silvia Montagna, Vanessa Orani, Raffaele Argiento

In professional tennis, it is often acknowledged that the server has an initial advantage. Indeed, the majority of points are won by the server, making the serve one of the most im…

stat.ME20192 cited

Is infinity that far? A Bayesian nonparametric perspective of finite mixture models

Raffaele Argiento, Maria De Iorio

Mixture models are one of the most widely used statistical tools when dealing with data from heterogeneous populations. This paper considers the long-standing debate over finite mi…

math.ST20152 cited

A priori truncation method for posterior sampling from homogeneous normalized completely random measure mixture models

Raffaele Argiento, Ilaria Bianchini, Alessandra Guglielmi

This paper adopts a Bayesian nonparametric mixture model where the mixing distribution belongs to the wide class of normalized homogeneous completely random measures. We propose a…