On Model Selection with Summary Statistics

Abstract

Recently, many authors have cast doubts on the validity of ABC model choice. It has been shown that the use of sufficient statistic in ABC model selection leads, apart from few exceptional cases in which the sufficient statistic is also cross-model sufficient, to unreliable results. In a single model context and given a sufficient summary statistic, we show that it is possible to fully recover the posterior normalising constant, without using the likelihood function. The idea can be applied, in an approximate way, to more realistic scenarios in which the sufficient statistic is not unavailable but a "good" summary statistic for estimation is available.