E. Dreassi

activity

20132026

most citedDisease Mapping via Negative Binomial Regression M-quantiles

33 citations · 34 across the 9 of their papers we have counts for

collaborators

10 papers

math.ST2026

Some cautionary tales about Bayesian predictive inference

Emanuela Dreassi, Fabrizio Leisen, Luca Pratelli +1

Two misunderstandings, frequently arising in Bayesian predictive inference, are discussed. The first deals with the data generating mechanism, while the second consists in overesti…

stat.ME2025

Bayesian nonparametric inference on a Fréchet class

Emanuela Dreassi, Luca Pratelli, Pietro Rigo

Let $(X, F, μ)$ and $(Y, G, ν)$ be probability spaces and $(Z_{n})$ a sequence of random variables with values in $(\mathcal{X}\times\mathcal{Y},\…

math.ST2024

Knockoffs for exchangeable categorical covariates

Emanuela Dreassi, Luca Pratelli, Pietro Rigo

Let $X = (X_{1}, \dots, X_{p})$ be a $p$ -variate random vector and $F$ a fixed finite set. In a number of applications, mainly in genetics, it turns out that $X_{i} \in F$ for each $i=1,\ldo…

math.ST2022

Generating knockoffs via conditional independence

Emanuela Dreassi, Fabrizio Leisen, Luca Pratelli +1

Let $X$ be a $p$ -variate random vector and $X$ a knockoff copy of $X$ (in the sense of \cite{CFJL18}). A new approach for constructing $X$ (henceforth, NA)…

stat.ME2022★ 1 cited

A probabilistic view on predictive constructions for Bayesian learning

Patrizia Berti, Emanuela Dreassi, Fabrizio Leisen +2

Given a sequence $X = (X_{1}, X_{2}, \dots)$ of random observations, a Bayesian forecaster aims to predict $X_{n + 1}$ based on $(X_{1}, \dots, X_{n})$ for each $n \geq 0$ . To this end, in princip…

math.PR2021

Kernel based Dirichlet sequences

Patrizia Berti, Emanuela Dreassi, Fabrizio Leisen +2

Let $X = (X_{1}, X_{2}, \dots)$ be a sequence of random variables with values in a standard space $(S, B)$ . Suppose \begin{gather*} X_1\simν\quad\text{and}\quad P\bigl(X_{n+1}\in…

math.ST2021

Bayesian predictive inference without a prior

Patrizia Berti, Emanuela Dreassi, Fabrizio Leisen +2

Let $(X_{n} : n \geq 1)$ be a sequence of random observations. Let $σ_{n} (\cdot) = P (X_{n + 1} \in \cdot ∣ X_{1}, \dots, X_{n})$ be the $n$ -th predictive distribution and $σ_0(\cdot)=P(X_…

math.ST2021

New perspectives on knockoffs construction

Patrizia Berti, Emanuela Dreassi, Fabrizio Leisen +2

Let $Λ$ be the collection of all probability distributions for $(X, X)$ , where $X$ is a fixed random vector and $X$ ranges over all possible knockoff copies…

stat.ME2014

A Bayesian semiparametric model for semicontinuous data

Emanuela Dreassi, Emilia Rocco

When the target variable exhibits a semicontinuous behaviour (i.e. a point mass in a single value and a continuous distribution elsewhere) parametric `two-part regression models' h…

stat.ME2013★ 33 cited

Disease Mapping via Negative Binomial Regression M-quantiles

Ray Chambers, Emanuela Dreassi, Nicola Salvati

We introduce a semi-parametric approach to ecological regression for disease mapping, based on modelling the regression M-quantiles of a Negative Binomial variable. The proposed me…