Robust approximate Bayesian inference
Methodology
2019-06-13 v3
Abstract
We discuss an approach for deriving robust posterior distributions from -estimating functions using Approximate Bayesian Computation (ABC) methods. In particular, we use -estimating functions to construct suitable summary statistics in ABC algorithms. The theoretical properties of the robust posterior distributions are discussed. Special attention is given to the application of the method to linear mixed models. Simulation results and an application to a clinical study demonstrate the usefulness of the method. An R implementation is also provided in the robustBLME package.
Cite
@article{arxiv.1706.01752,
title = {Robust approximate Bayesian inference},
author = {Erlis Ruli and Nicola Sartori and Laura Ventura},
journal= {arXiv preprint arXiv:1706.01752},
year = {2019}
}
Comments
This is a revised and personal manuscript version of the article that has been accepted for publication by Journal of Statistical Planning and Inference