activity
20222026
most citedA note on auxiliary mixture sampling for Bayesian Poisson models

2 citations · 7 across the 7 of their papers we have counts for

collaborators

7 papers

stat.ME2026

Coarsening-adjusted estimators for finite-population indicators

Gaia Bertarelli, Aldo Gardini

Self-reported numerical variables in sample surveys are frequently subject to coarsening, as respondents tend to report rounded or heaped values rather than the exact underlying qu…

physics.ins-det2026

A rapid low-background assay of Pb in archaeological lead

M. Consonni, M. Clemenza, E. Di Stefano +11

In this work, we present a fast and highly efficient method for the measurement of Pb in metallic archaeological lead using the commercial low-background liquid scintillati…

stat.ME2026

Coarsened data in small area estimation: a Bayesian two-part model for mapping smoking behaviour

Aldo Gardini, Lorenzo Mori

Estimating health indicators for restricted sub-populations is a recurring challenge in epidemiology and public health. When survey data are used, Small Area Estimation (SAE) metho…

stat.ME2025★ 2 cited

A note on auxiliary mixture sampling for Bayesian Poisson models

Aldo Gardini, Fedele Greco, Carlo Trivisano

Bayesian hierarchical Poisson models are an essential tool for analyzing count data. However, designing efficient algorithms to sample from the posterior distribution of the target…

stat.ME2023★ 2 cited

Functional and variables selection in extreme value models for regional flood frequency analysis

Aldo Gardini

The problem of estimating return levels of river discharge, relevant in flood frequency analysis, is tackled by relying on the extreme value theory. The Generalized Extreme Value (…

stat.ME2023★ 1 cited

Mapping poverty at multiple geographical scales

Silvia De Nicolò, Enrico Fabrizi, Aldo Gardini

Poverty mapping is a powerful tool to study the geography of poverty. The choice of the spatial resolution is central as poverty measures defined at a coarser level may mask their…

stat.ME2022★ 2 cited

Design and Structure Dependent Priors for Scale Parameters in Latent Gaussian Models

Aldo Gardini, Fedele Greco, Carlo Trivisano

Many common correlation structures assumed for data can be described through latent Gaussian models. When Bayesian inference is carried out, it is required to set the prior distrib…