A0745
Title: Robust Bayesian exponentially tilted empirical likelihood
Authors: Zhichao Liu - Monash University (Australia) [presenting]
Catherine Forbes - Monash University (Australia)
Abstract: A new robust Bayesian exponentially tilted empirical likelihood (RBETEL) inferential methodology is proposed which is suitable for moment condition models for data that may be contaminated by outliers. The foundations are on the original Bayesian exponentially tilted empirical likelihood (BETEL) method, justified by the fact that an empirical likelihood can be interpreted as the nonparametric limit of a Bayesian procedure when the implied probabilities are obtained from maximizing entropy subject to some given moment constraints. After first demonstrating through a simulation exercise that BETEL posteriors are susceptible to interference from outliers, a Markov chain Monte Carlo framework is developed, incorporating outlier information derived from a robust frequentist estimator of multivariate location and scatter. Controlled simulation experiments are conducted to investigate the performance of the proposed RBETEL method, and finds that the new approach improves on BETEL when outliers are present.
