Work

Clustering blood donors via mixtures of product partition models with covariates

Year: 2024

Type: article

Abstract: 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. These models allow for the prediction of new recurrences, accommodating covariate information that describes the personal characteristics of the sample individuals. We introduce a prior for the random partition of the sample individuals, which encourages two individuals to be co-clustered if they have similar covariate values. Our prior generalizes product partition models with covariates (PPMx) models in the literature, which are defined in terms of cohesion and similarity functions. We assume cohesion functions that yield mixtures of PPMx models, while our similarity functions represent the denseness of a cluster. We show that including covariate information in the prior specification improves the posterior predictive performance and helps interpret the estimated clusters in terms of covariates in the blood donation application.

Source: Biometrics

DOI: 10.1093/biomtc/ujad021

Authors: Raffaele Argiento, Riccardo Corradin, Alessandra Guglielmi, Ettore Lanzarone

Institutions: University of Bergamo, University of Nottingham, Politecnico di Milano

Language: English

Topic: Bayesian Methods and Mixture Models

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,969

Funder: European Commission