Can I read Robust likelihood functions in Bayesian inference on EtoBox?

Robust likelihood functions in Bayesian inference by Luca Greco; Walter Racugno; Laura Ventura is a Mathematics article available to read on EtoBox.

What is Robust likelihood functions in Bayesian inference about?

In order to deal with mild deviations from the assumed parametric model, we propose a procedure for accounting for model uncertainty in the Bayesian framework. In particular, in the derivation of posterior distributions, we discuss the use of robust pseudo-likelihoods, which offer the advantage of preventing the effects caused by model misspecifications, i.e. when the underlying distribution lies in a neighborhood of the assumed model. The influence functions of posterior summaries, such as the posterior mean, are investigated as well as the asymptotic properties of robust posterior distributions. Although the use of a pseudo-likelihood cannot be considered orthodox in the Bayesian perspective, it is shown that, also through some illustrative examples, how a robust pseudo-likelihood, with the same asymptotic properties of a genuine likelihood, can be useful in the inferential process in order to prevent the effects caused by model misspecifications.

Who reads Robust likelihood functions in Bayesian inference?

Author

Luca Greco; Walter Racugno; Laura Ventura

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0378-3758)

Published

2008

Language

EN

Field

Mathematics (Physical Sciences)