2 citations · 3 across the 9 of their papers we have counts for
7 papers · 1 filter
Nonparametric Bayesian inference for the Gini-Simpson index
Pier Giovanni Bissiri, Riccardo Corradin, Andrea Ongaro
Many statistical problems concern the analysis of species distributions or, more generally, of discrete labeled quantities. Assessing species diversity constitutes a key step towar…
Model-based clustering of time-dependent observations with common structural changes
Riccardo Corradin, Luca Danese, Wasiur R. KhudaBukhsh +1
We propose a novel model-based clustering approach for samples of time series. We assume as a unique commonality that two observations belong to the same group if structural change…
Nested Compound Random Measures
Federico Camerlenghi, Riccardo Corradin, Andrea Ongaro
Nested nonparametric processes are vectors of random probability measures widely used in the Bayesian literature to model the dependence across distinct, though related, groups of…
Clustering blood donors via mixtures of product partition models with covariates
Raffaele Argiento, Riccardo Corradin, Alessandra Guglielmi +1
Motivated by the problem of accurately predicting gap times between successive blood donations, we present here a general class of Bayesian nonparametric models for clustering. The…
Contaminated Gibbs-type priors
Federico Camerlenghi, Riccardo Corradin, Andrea Ongaro
Gibbs-type priors are widely used as key components in several Bayesian nonparametric models. By virtue of their flexibility and mathematical tractability, they turn out to be pred…
Optimal stratification of survival data via Bayesian nonparametric mixtures
Riccardo Corradin, Luis Enrique Nieto-Barajas, Bernardo Nipoti
The stratified proportional hazards model represents a simple solution to account for heterogeneity within the data while keeping the multiplicative effect on the hazard function.…
Dirichlet process mixtures under affine transformations of the data
Julyan Arbel, Riccardo Corradin, Bernardo Nipoti
Location-scale Dirichlet process mixtures of Gaussians (DPM-G) have proved extremely useful in dealing with density estimation and clustering problems in a wide range of domains. M…