Sampling models for selective inference

Daniel Garcia Rasines; G. Alastair Young

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arXiv · 2502.02213

Sampling models for selective inference

Daniel Garcia Rasines·G. Alastair Young

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Abstract

This paper explores the challenges of constructing suitable inferential models in scenarios where the parameter of interest is determined in light of the data, such as regression after variable selection. Two compelling arguments for conditioning converge in this context, whose interplay can introduce ambiguity in the choice of conditioning strategy: the Conditionality Principle, from classical statistics, and the `condition on selection' paradigm, central to selective inference. We discuss two general principles that can be employed to resolve this ambiguity in some recurrent contexts. The first one refers to the consideration of how information is processed at the selection stage. The second one concerns an exploration of ancillarity in the presence of selection. We demonstrate that certain notions of ancillarity are preserved after conditioning on the selection event, supporting the application of the Conditionality Principle. We illustrate these concepts through examples and provide guidance on the adequate inferential approach in some common scenarios.

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Daniel Garcia Rasines, G. Alastair Young. 2025-07-19. Sampling models for selective inference. https://arxiv.org/abs/2502.02213

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