63 citations · 146 across the 10 of their papers we have counts for
10 papers
On approximate robust confidence distributions
Elena Bortolato, Laura Ventura
A confidence distribution is a complete tool for making frequentist inference for a parameter of interest based on an assumed parametric model. Indeed, it allows to reach point…
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…
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…
Objective Bayesian inference with proper scoring rules
Federica Giummolè, Valentina Mameli, Erlis Ruli +1
Standard Bayesian analyses can be difficult to perform when the full likelihood, and consequently the full posterior distribution, is too complex and difficult to specify or if rob…
Robust approximate Bayesian inference
Erlis Ruli, Nicola Sartori, Laura Ventura
We discuss an approach for deriving robust posterior distributions from -estimating functions using Approximate Bayesian Computation (ABC) methods. In particular, we use -est…
Improved Laplace Approximation for Marginal Likelihoods
Erlis Ruli, Nicola Sartori, Laura Ventura
Statistical applications often involve the calculation of intractable multidimensional integrals. The Laplace formula is widely used to approximate such integrals. However, in high…
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…
Approximate Bayesian Computation with composite score functions
Erlis Ruli, Nicola Sartori, Laura Ventura
Both Approximate Bayesian Computation (ABC) and composite likelihood methods are useful for Bayesian and frequentist inference, respectively, when the likelihood function is intrac…
A note on marginal posterior simulation via higher-order tail area approximations
Erlis Ruli, Nicola Sartori, Laura Ventura
We explore the use of higher-order tail area approximations for Bayesian simulation. These approximations give rise to an alternative simulation scheme to MCMC for Bayesian computa…