arXiv subjects

Ilenia Epifani

Publications and source records attributed to Ilenia Epifani.

3 recordsLinked to original sources

arXiv2026

A warning system for risk prediction of metabolic syndrome in a healthy population of blood donors

Metabolic syndrome is a complex clinical condition characterized by the simultaneous presence of multiple metabolic risk factors and represents a major public health concern. The syndrome develops silently and may remain undiagnosed for long periods, highlighting the importance of investigating early metabolic alterations before overt disease onset. Longitudinal monitoring of predominantly healthy individuals may help identify metabolic risk early. The paper proposes a Bayesian statistical model to estimate the probability of metabolic syndrome among blood donors during pre-donation screening, incorporating information collected at previous visits. Using longitudinal data from one of the main blood donor associations in Italy, AVIS Milan, we analyze repeated clinical and lifestyle measurements from a predominantly healthy population of donors. In particular, we fit a Bayesian multivariate model that jointly represents the logarithm of the five diagnostic components of metabolic syndrome. The model accounts for within-donor dependence across repeated visits and provides probabilistic estimates of individual risk. Our framework aims to provide clinicians at AVIS Milan with an interpretable traffic-light warning system (low, intermediate, high risk) during pre-donation screening to facilitate the identification of individuals at risk of metabolic syndrome at future visits and to support targeted preventive interventions during routine donor assessment, ultimately contributing to a long-term reduction in healthcare costs for the Italian national healthcare system.

stat.AP↗

arXiv2025

Robust Probabilistic Model Checking with Continuous Reward Domains

Probabilistic model checking traditionally verifies properties on the expected value of a measure of interest. This restriction may fail to capture the quality of service of a significant proportion of a system's runs, especially when the probability distribution of the measure of interest is poorly represented by its expected value due to heavy-tail behaviors or multiple modalities. Recent works inspired by distributional reinforcement learning use discrete histograms to approximate integer reward distribution, but they struggle with continuous reward space and present challenges in balancing accuracy and scalability. We propose a novel method for handling both continuous and discrete reward distributions in Discrete Time Markov Chains using moment matching with Erlang mixtures. By analytically deriving higher-order moments through Moment Generating Functions, our method approximates the reward distribution with theoretically bounded error while preserving the statistical properties of the true distribution. This detailed distributional insight enables the formulation and robust model checking of quality properties based on the entire reward distribution function, rather than restricting to its expected value. We include a theoretical foundation ensuring bounded approximation errors, along with an experimental evaluation demonstrating our method's accuracy and scalability in practical model-checking problems.

cs.AI↗

arXiv2008

Case-deletion importance sampling estimators: Central limit theorems and related results

Case-deleted analysis is a popular method for evaluating the influence of a subset of cases on inference. The use of Monte Carlo estimation strategies in complicated Bayesian settings leads naturally to the use of importance sampling techniques to assess the divergence between full-data and case-deleted posteriors and to provide estimates under the case-deleted posteriors. However, the dependability of the importance sampling estimators depends critically on the variability of the case-deleted weights. We provide theoretical results concerning the assessment of the dependability of case-deleted importance sampling estimators in several Bayesian models. In particular, these results allow us to establish whether or not the estimators satisfy a central limit theorem. Because the conditions we derive are of a simple analytical nature, the assessment of the dependability of the estimators can be verified routinely before estimation is performed. We illustrate the use of the results in several examples.

stat.CO↗