114 citations · 302 across the 21 of their papers we have counts for
24 papers
Bayesian Discrepancy Measure: Higher-order and Skewed approximations
Elena Bortolato, Francesco Bertolino, Monica Musio +1
The aim of this paper is to discuss both higher-order asymptotic expansions and skewed approximations for the Bayesian Discrepancy Measure for testing precise statistical hypothese…
Individual Causation with Biased Data
Monica Musio, Philip Dawid
We consider the problem of assessing whether, in an individual case, there is a causal relationship between an observed exposure and a response variable. When data are available on…
A scientometric analysis of the effect of COVID-19 on the spread of research outputs
Gianpaolo Zammarchi, Andrea Carta, Silvia Columbu +2
The spread of the Sars-COV-2 pandemic in 2020 had a huge impact on the life course of all of us. This rapid spread has also caused an increase in the research production in topics…
Comparison of two coefficients of variation: a new Bayesian approach
Francesco Bertolino, Silvia Columbu, Mara Manca +1
The coefficient of variation is a useful indicator for comparing the spread of values between dataset with different units or widely different means. In this paper we address the p…
Robust confidence distributions from proper scoring rules
Erlis Ruli, Laura Ventura, Monica Musio
A confidence distribution is a distribution for a parameter of interest based on a parametric statistical model. As such, it serves the same purpose for frequentist statisticians a…
A new Bayesian discrepancy measure
Francesco Bertolino, Mara Manca, Monica Musio +2
The aim of this article is to make a contribution to the Bayesian procedure of testing precise hypotheses for parametric models. For this purpose, we define the Bayesian Discrepanc…
Interpreting the outcomes of research assessments: a geometrical approach
Beniamino Cappelletti-Montano, Silvia Columbu, Stefano Montaldo +1
Research evaluations and comparison of the assessments of academic institutions (scientific areas, departments, universities etc.) are among the major issues in recent years in hig…
Effects of Causes and Causes of Effects
A. Philip Dawid, Monica Musio
We describe and contrast two distinct problem areas for statistical causality: studying the likely effects of an intervention ("effects of causes"), and studying whether there is a…
Robust inference for nonlinear regression models from the Tsallis score: application to Covid-19 contagion in Italy
Paolo Girardi, Luca Greco, Valentina Mameli +4
We discuss an approach for fitting robust nonlinear regression models, which can be employed to model and predict the contagion dynamics of the Covid-19 in Italy. The focus is on t…
Bounding Causes of Effects with Mediators
Philip Dawid, Macartan Humphreys, Monica Musio
Suppose X and Y are binary exposure and outcome variables, and we have full knowledge of the distribution of Y, given application of X. From this we know the average causal effect…
The Hyvärinen scoring rule in Gaussian linear time series models
Silvia Columbu, Valentina Mameli, Monica Musio +1
Likelihood-based estimation methods involve the normalising constant of the model distributions, expressed as a function of the parameter. However in many problems this function is…
Causes of Effects via a Bayesian Model Selection Procedure
Fabio Corradi, Monica Musio
In causal inference, and specifically in the \textit{Causes of Effects} problem, one is interested in how to use statistical evidence to understand causation in an individual case,…
The Probability of Causation
Philip Dawid, Monica Musio, Rossella Murtas
Many legal cases require decisions about causality, responsibility or blame, and these may be based on statistical data. However, causal inferences from such data are beset by subt…
New bounds for the Probability of Causation in Mediation Analysis
Rossella Murtas, Alexander Philip Dawid, Monica Musio
An individual has been subjected to some exposure and has developed some outcome. Using data on similar individuals, we wish to evaluate, for this case, the probability that the ou…
A Note on Bayesian Model Selection for Discrete Data Using Proper Scoring Rules
A. Philip Dawid, Monica Musio, Silvia Columbu
We consider the problem of choosing between parametric models for a discrete observable, taking a Bayesian approach in which the within-model prior distributions are allowed to be…
Rejoinder to "Bayesian Model Selection Based on Proper Scoring Rules"
A. Philip Dawid, Monica Musio
We are deeply appreciative of the initiative of the editor, Marina Vanucci, in commissioning a discussion of our paper, and extremely grateful to all the discussants for their insi…
Bounding the Probability of Causation in Mediation Analysis
A. P. Dawid, R. Murtas, M. Musio
Given empirical evidence for the dependence of an outcome variable on an exposure variable, we can typically only provide bounds for the "probability of causation" in the case of a…
Comparisons of Hyvärinen and pairwise estimators in two simple linear time series models
Valentina Mameli, Monica Musio, A. Philip Dawid
The aim of this paper is to compare numerically the performance of two estimators based on Hyvärinen's local homogeneous scoring rule with that of the full and the pairwise maximum…
Bayesian Model Selection Based on Proper Scoring Rules
A. Philip Dawid, Monica Musio
Bayesian model selection with improper priors is not well-defined because of the dependence of the marginal likelihood on the arbitrary scaling constants of the within-model prior…
Minimum scoring rule inference
Philip Dawid, Monica Musio, Laura Ventura
Proper scoring rules are methods for encouraging honest assessment of probability distributions. Just like likelihood, a proper scoring rule can be applied to supply an unbiased es…
Theory and Applications of Proper Scoring Rules
A. Philip Dawid, Monica Musio
We give an overview of some uses of proper scoring rules in statistical inference, including frequentist estimation theory and Bayesian model selection with improper priors.
From Statistical Evidence to Evidence of Causality
Philip Dawid, Monica Musio, Stephen E. Fienberg
While statisticians and quantitative social scientists typically study the "effects of causes" (EoC), Lawyers and the Courts are more concerned with understanding the "causes of ef…
A Generalization of the Skew-Normal Distribution: The Beta Skew-Normal
Valentina Mameli, Monica Musio
The aim of this article is to introduce a new family of distributions, which generalizes the skew normal distribution (SN). This new family, called Beta skew-normal (BSN), arises n…
Using Integrated Nested Laplace Approximation for Modeling Spatial Healthcare Utilization
Erik A. Sauleau, Valentina Mameli, Monica Musio
In recent years, spatial and spatio-temporal modeling have become an important area of research in many fields (epidemiology, environmental studies, disease mapping). In this work…