Pré-Publication, Document De Travail Année : 2025

Review of post-clustering inference methods

Résumé

In a classical testing problem, statistical hypotheses are defined before observing the data. However, this principle is violated when hypotheses are based on a clustering of the observations, leading to an inflation of the type I error rate. Recently, post-clustering inference methods have been proposed to address this particular type of data-driven hypothesis selection. These methods are divided in two categories: information partitioning and conditional approaches. This article provides a review of these methods, and a numerical comparison on simulated Gaussian data. This contribution sheds some light on the pros and cons of each approach in terms of (i) type I error control and power, (ii) potential dependence on a specific clustering method, and (iii) assumptions on data distribution.

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Dates et versions

hal-05053220 , version 1 (06-05-2025)
hal-05053220 , version 2 (23-03-2026)

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  • HAL Id : hal-05053220 , version 1

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Nicolas Enjalbert Courrech, Cathy Maugis-Rabusseau, Neuvial Pierre. Review of post-clustering inference methods. 2025. ⟨hal-05053220v1⟩
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