We discuss and characterise connections between frequentist, confidence distribution and
objective Bayesian inference, when considering higher-order asymptotics, matching priors …

A confidence distribution is a complete tool for making frequentist inference for a parameter
of interest based on an assumed parametric model. Indeed, it provides point estimates …

The interplay between Bayesian and frequentist inference can play a remarkable role in
order to address some theoretical and computational drawbacks, due to the complexity or …

This paper aims to contribute to refining the e-values for testing precise hypotheses,
especially when dealing with nuisance parameters, leveraging the effectiveness of …

The aim of this contribution is to discuss approximate Bayesian computation based on the
asymptotic theory of modified likelihood roots and log-likelihood ratios. Results on third …

A new method for posterior simulation is proposed, based on the combination of higher-
order asymptotic results with the inverse transform sampler. This method can be used to …

The full Bayesian significance test (FBST) was introduced by Pereira and Stern for
measuring the evidence of a precise null hypothesis. The FBST requires both numerical …

Higher-order asymptotic arguments for a scalar parameter of interest have been widely
investigated for Bayesian inference. In this paper the theory of asymptotic expansions is …

The process of inference in a parametric statistical model involves assessing the uncertainty
of the surrounding parameters based on an observed sample. Often, the lack of analytical …

L'utilizzo di metodi bayesiani nella teoria e nella pratica statistica ha avuto un forte
incremento in questi ultimi anni. La loro implementazione richiede la specificazione di una …