Robust approximate Bayesian inference
| Autor: | Ruli, Erlis, Sartori, Nicola, Ventura, Laura |
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| Rok vydání: | 2017 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1016/j.jspi.2019.05.006 |
| Popis: | We discuss an approach for deriving robust posterior distributions from $M$-estimating functions using Approximate Bayesian Computation (ABC) methods. In particular, we use $M$-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. Comment: This is a revised and personal manuscript version of the article that has been accepted for publication by Journal of Statistical Planning and Inference |
| Databáze: | arXiv |
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