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Preprints, Working Papers, ... Year : 2023

CONTAMINATION-SOURCE BASED K-SAMPLE CLUSTERING

Abstract

We investigate in this work the K-sample clustering of populations issued from contamination phenomenon. A contamination model is a two-component mixture model in which one component is known (standard behaviour) when the second one, modelling a departure from the standard behaviour, is unknown. When K populations from such a model are observed we propose a semiparametric clustering methodology to detect, for coordinated diagnosis and/or best practices sharing purpose, which populations are impacted by the same type of contamination. We prove the consistency of our approach under the existence of true clusters and show the performances of our methodology through an extensive Monte Carlo study. We finally apply our methodology, implemented in the admix R package, to a European countries COVID-19 excess of mortality dataset for which we aim to cluster countries similarly impacted by the pandemic over classes of age.
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Dates and versions

hal-04129130 , version 1 (15-06-2023)

Licence

Public Domain

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  • HAL Id : hal-04129130 , version 1

Cite

Xavier Milhaud, Denys Pommeret, Yahia Salhi, Pierre Vandekerkhove. CONTAMINATION-SOURCE BASED K-SAMPLE CLUSTERING. 2023. ⟨hal-04129130⟩
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