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A0276
Title: Selective inference Authors:  Alastair Young - Imperial College London (United Kingdom) [presenting]
Abstract: Selective inference is concerned with performing valid statistical inference when the questions being addressed are suggested by examination of data, rather than being specified before data collection. We describe key ideas in selective inference, from both frequentist and Bayesian perspectives. In frequentist analysis, the fundamental notion is that valid inference, in the sense of control of error rates, is only obtained by conditioning on the selection event, that is, by considering hypothetical repetitions which lead to the same inferential questions being asked. The Bayesian standpoint is less clear, but it may be argued that such conditioning on the selection is required if this takes place on the parameter space as well as on the sample space. We provide an overview of conceptual and computational challenges, as well as asymptotic properties of selective inference in both frameworks, under the assumption that selection is made in a well-defined way.