Title: An easy-to-use empirical likelihood ABC method
Authors: Sanjay Chaudhuri - National University of Singapore (Singapore) [presenting]
Subhroshekhar Ghosh - National University of Singapore (Singapore)
David Nott - National University of Singapore (Singapore)
Kim Cuc Pham - National University of Singapore (Singapore)
Abstract: Many scientifically well-motivated statistical models in natural, engineering and environmental sciences are specified through a generative process, but in some cases, it may not be possible to write down a likelihood for these models analytically. Approximate Bayesian computation (ABC) methods, which allow Bayesian inference in these situations, are typically computationally intensive. Recently, computationally attractive empirical likelihood based ABC methods have been suggested in the literature. These methods heavily rely on the availability of a set of suitable analytically tractable estimating equations. We propose an easy-to-use empirical likelihood ABC method, where the only inputs required are a choice of summary statistic, its observed value, and the ability to simulate summary statistics for any parameter value under the model. It is shown that the posterior obtained using the proposed method is consistent, and its performance is explored using various examples.