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Selective inference after convex clustering with $\ell_1$ penalizationSelective inference after convex clustering with $\ell_1$ penalization

François Bachoc · Cathy Maugis-Rabusseau · Pierre Neuvial首次提交 2023-09-04 · 修订 2025-11-09 · v2arXiv预印本

ℓ1 惩罚凸聚类的选择性推断AI 解读

Classical inference methods notoriously fail when applied to data-driven test hypotheses or inference targets. Instead, dedicated methodologies are required to obtain statistical guarantees for these selective inference problems. Selective inference is particularly relevant post-clustering, typically when testing a difference in mean between two clusters. In this paper, we address convex clustering with $\ell_1$ penalization, by leveraging related selective inference tools…Classical inference methods notoriously fail when applied to data-driven test hypotheses or inference targets. Instead, dedicated methodologies are required to obtain statistical guarantees for these selective inference problems. Selective inference is particularly relevant post-clustering, typically when testing a difference in mean between two clusters. In this paper, we address convex clustering with $\ell_1$ penalization, by leveraging related selective inference tools…

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Selective inference after convex clustering with ℓ1\ell_1ℓ1​ penalizationSelective inference after convex clustering with $\ell_1$ penalization

Selective inference after convex clustering with $\ell_1$ penalizationSelective inference after convex clustering with $\ell_1$ penalization