Post-clustering difference testing: valid inference and practical considerations with applications to ecological and biological data

DOI10.1016/j.csda.2023.107916zbMath1543.62088MaRDI QIDQ6561255

Benjamin Hivert, Rodolphe Thiébaut, Boris P. Hejblum, Denis Agniel

Publication date: 25 June 2024

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

zbMATH Keywords

clusteringmultimodality testselective inferencedouble-dipping

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08)

Cites Work

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• Exact post-selection inference, with application to the Lasso
• The dip test of unimodality
• Multidimensional two-component Gaussian mixtures detection
• Permutation \(p\)-values should never be zero: calculating exact \(p\)-values when permutations are randomly drawn
• Pairwise Variable Selection for High-Dimensional Model-Based Clustering
• Statistical Significance of Clustering for High-Dimension, Low–Sample Size Data
• Variable Selection for Clustering with Gaussian Mixture Models
• Excess Mass Estimates and Tests for Multimodality
• Combining p-values via averaging
• Model-Based Clustering and Classification for Data Science
• Variable Selection for Model-Based Clustering
• Statistical Significance for Hierarchical Clustering

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