Predictive inference for non-probability samples

Non-probability samples provide a challenging source of information for official statistics, because the data generating mechanism is unknown. Making inference from such samples therefore requires a novel approach compared with the classic approach of survey sampling. We propose a framework based on predictive inference and discuss three classes of methods. We conduct a simulation study with a real-world data set.